Interactive interfaces to present data arrangement overviews and summarized dataset attributes for collaborative datasets

ABSTRACT

Various embodiments relate generally to data science and data analysis, and computer software and systems, to provide an interface between repositories of disparate datasets and computing machine-based entities that seek access to the datasets, and, more specifically, to a computing and data storage platform that facilitates consolidation of one or more datasets, whereby user interfaces may be implemented as computerized tools for presenting summarization of dataset attributes to facilitate discovery, formation, and analysis of interrelated collaborative datasets. In some examples, a method may include receiving data resulting from insight calculations. Insight calculations may be based on a derived dataset attribute. Also, the method may include presenting a data arrangement overview summarizing the data attributes as an aggregation of data attributes in a portion of the user interface. The data arrangement overview may include an interactive display of a distribution associated with a collaborative atomized dataset.

CROSS-REFERENCE TO APPLICATIONS

This application is a continuation application of copending U.S. patentapplication Ser. No. 16/917,228, filed Jun. 30, 2022 and titled,“INTERACTIVE INTERFACES TO PRESENT DATA ARRANGEMENT OVERVIEWS ANDSUMMARIZED DATA ATTRIBUTES FOR COLLABORATIVE DATASETS,” U.S. patentapplication Ser. No. 16/917,228 is a continuation application of U.S.patent application Ser. No. 15/454,969, filed on Mar. 9, 2017, now U.S.Pat. No. 10,747,774 and titled, “INTERACTIVE INTERFACES TO PRESENT DATAARRANGEMENT OVERVIEWS AND SUMMARIZED DATASET ATTRIBUTES FORCOLLABORATIVE DATASETS,” U.S. patent application Ser. No. 15/454,969 isa continuation-in-part application of U.S. patent application Ser. No.15/186,514, filed on Jun. 19, 2016, now U.S. Pat. No. 10,102,258 andtitled “COLLABORATIVE DATASET CONSOLIDATION VIA DISTRIBUTED COMPUTERNETWORKS,” all of which are herein incorporated by reference in theirentirety for all purposes.

FIELD

Various embodiments relate generally to data science and data analysis,computer software and systems, and wired and wireless networkcommunications to provide an interface between repositories of disparatedatasets and computing machine-based entities that seek access to thedatasets, and, more specifically, to a computing and data storageplatform that facilitates consolidation of one or more datasets, wherebyone or more interfaces, such as user interfaces, may be implemented ascomputerized tools for presenting summarization of dataset attributes tofacilitate discovery, formation, and analysis of interrelatedcollaborative datasets.

BACKGROUND

Advances in computing hardware and software have fueled exponentialgrowth in the generation of vast amounts of data due to increasedcomputations and analyses in numerous areas, such as in the variousscientific and engineering disciplines, as well as in the application ofdata science techniques to endeavors of good-will (e.g., areas ofhumanitarian, environmental, medical, social, etc.). Also, advances inconventional data storage technologies provide the ability to store theincreasing amounts of generated data. Consequently, traditional datastorage and computing technologies have given rise to a phenomenon inwhich numerous disparate datasets have reached sizes and complexitiesthat traditional data-accessing and analytic techniques are generallynot well-suited for assessing conventional datasets.

Conventional technologies for implementing datasets typically rely ondifferent computing platforms and systems, different databasetechnologies, and different data formats, such as CSV, TSV, HTML, JSON,XML, etc. Further, known data-distributing technologies are notwell-suited to enable interoperability among datasets. Thus, manytypical datasets are warehoused in conventional data stores, which aregenerally “data silos,” whereby data in the associated data stores areoften difficult to connect to other sources of data. These data siloshave inherent barriers that insulate and isolate datasets. Further,conventional data systems and dataset accessing techniques are generallyincompatible or inadequate to facilitate data interoperability among thedata silos.

Conventional approaches to provide dataset generation and management,while functional, suffer a number of other drawbacks. For example,disparate approaches to gathering, forming, and analyzing datasetstypically require different, ad hoc approaches. For example, datascientists and other consumers of data generally undertake significanteffort during a variety of steps in which a dataset is downloaded andanalyzed. In particular, data practitioners usually perform personalizedqueries and data analyses, manually, on the downloaded dataset todetermine whether the downloaded dataset is of any use. Contextualinformation for understanding the downloaded dataset is usually absent,due to the ad hoc nature of dataset development, thereby complicatingthe process by which data practitioners assess the worthiness of adataset. Further, differently-formatted repositories of data providefurther challenges when assessing multiple dataset with multipleversions of ad hoc queries. Hence, these approaches are not typicallywell-suited to resolve sufficiently the drawbacks of traditionaltechniques of dataset generation and analysis. Moreover, traditionaldataset generation and management are not well-suited to reducingefforts by data scientists and data practitioners in extracting,transforming, and loading data into data stores in a manner that servestheir desired objectives.

Thus, what is needed is a solution for facilitating techniques todiscover, form, and analyze datasets, without the limitations ofconventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention aredisclosed in the following detailed description and the accompanyingdrawings:

FIG. 1 is a diagram depicting computerized tools to discover, form,and/or analyze collaborative datasets, according to some embodiments;

FIG. 2 is a diagram depicting an example of programmatic interface,according to some examples;

FIG. 3 is a diagram depicting a flow diagram as an example ofcollaborative dataset creation, according to some embodiments;

FIG. 4 is a diagram depicting a collaborative dataset consolidationsystem, according to some embodiments;

FIG. 5A is a diagram depicting an example of an atomized data point,according to some embodiments;

FIG. 5B is a diagram depicting operation an example of a collaborativedataset consolidation system, according to some examples;

FIG. 6 is a diagram depicting an example of a dataset analyzer and aninference engine, according to some embodiments;

FIG. 7 is a diagram depicting operation of an example of an inferenceengine, according to some embodiments;

FIG. 8 is a diagram depicting a flow diagram as an example of ingestingan enhanced dataset into a collaborative dataset consolidation system,according to some embodiments;

FIG. 9 is a diagram depicting a dataset creation interface, according tosome embodiments;

FIG. 10 is a diagram of an example of a user interface depictingprogression of phases during creation of a dataset, according to someembodiments;

FIG. 11 is a diagram of an example of a user interface configured toenhance dataset attribute data for a dataset, according to someembodiments;

FIG. 12 is a diagram depicting an example of a data ingestion controllerconfigured to generate a set of layer data files, according to someexamples;

FIG. 13 is a diagram depicting a user interface in association withgeneration and presentation of the derived subset of data, according tosome examples;

FIGS. 14 and 15 are diagrams depicting examples of generating andpresenting derived columns and derived data, according to some examples;

FIG. 16 is a diagram depicting a flow diagram as an example of enhancedcollaborative dataset creation based on a derived dataset attribute,according to some embodiments;

FIG. 17 is a diagram depicting an example of a collaboration managerconfigured to present collaborative information regarding collaborativedatasets, according to some embodiments;

FIG. 18A depicts an example of a dataset attribute manager configured togenerate data to enhance datasets, according to some examples;

FIGS. 18B and 18C are diagrams that depict examples of calculators todetermine trend data and relevancy data relating to collaborativedatasets, according to some examples;

FIG. 19 is a diagram depicting an example of a dataset activity feed topresent dataset interaction control elements in a user interface,according to some embodiments;

FIG. 20 is a diagram depicting other examples of dataset activity feedsto present a dataset recommendation feed, according to some embodiments;

FIG. 21 is a diagram depicting examples of trend-related datasetactivity feeds to facilitate presentation and interaction with userinterface elements, according to some embodiments;

FIG. 22 is a diagram depicting other examples of relevancy-relateddataset activity feeds to facilitate presentation and interaction withuser interface elements, according to some embodiments;

FIG. 23 is an example of a data entry interface to access atomizeddatasets, according to some examples;

FIG. 24 is an example of a user interface to present interactive userinterface elements to provide a data overview of a dataset, according tosome examples;

FIG. 25 is an example of a user interface to present interactive userinterface elements for another data preview of a dataset, according tosome examples;

FIG. 26 is a diagram depicting a flow diagram to present interactiveuser interface elements for a data overview of a dataset, according tosome embodiments;

FIG. 27 is an example of a user interface to present interactive userinterface elements for conveying summary characteristics of a dataset,according to some examples;

FIG. 28 is a diagram depicting a flow diagram to present summarycharacteristics for a dataset in an interactive overlay window,according to some embodiments;

FIG. 29 is a diagram depicting an example in which a subset of data maybe analyzed to determine a graphical representation of the datadistribution, according to some examples;

FIGS. 30A to 30F are diagrams depicting examples of interactive overlaywindows, according to some examples;

FIG. 32 is a diagram depicting an example of a dataset access interface,according to some examples;

FIG. 33 is a diagram depicting a flow diagram to implement a datasetaccess interface, according to some embodiments; and

FIG. 34 illustrates examples of various computing platforms configuredto provide various functionalities to components of a collaborativedataset consolidation system, according to various embodiments.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, an apparatus, a user interface, or aseries of program instructions on a computer readable medium such as acomputer readable storage medium or a computer network where the programinstructions are sent over optical, electronic, or wirelesscommunication links. In general, operations of disclosed processes maybe performed in an arbitrary order, unless otherwise provided in theclaims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims, and numerousalternatives, modifications, and equivalents thereof. Numerous specificdetails are set forth in the following description in order to provide athorough understanding. These details are provided for the purpose ofexample and the described techniques may be practiced according to theclaims without some or all of these specific details. For clarity,technical material that is known in the technical fields related to theexamples has not been described in detail to avoid unnecessarilyobscuring the description.

FIG. 1 is a diagram depicting computerized tools to discover, form,and/or analyze collaborative datasets, according to some embodiments.Diagram 100 depicts an example of a subset of user interfaces tofacilitate implementation of computerized tools at a computing device109 a (as well as computing devices 102 a, 102 b, and 102 n) or acollaborative dataset consolidation system 110, or both. Computingdevice 109 a may be configured to interoperate via a programmaticinterface 190 with collaborative dataset consolidation system 110.Programmatic interface 190 may be configured to facilitatefunctionalities of user interfaces 102, 122, and 132, and furtherconfigured to facilitate data exchanges with collaborative datasetconsolidation system 100, and among computing device 109 a and computingdevices 102 a, 102 b, and 102 n.

A first example of a computerized tool is shown implemented as datasetcreation interface 102, which may be configured to create a dataset,according to various embodiments. A collaborative dataset, according tosome non-limiting examples, is a set of data that may be configured tofacilitate data interoperability over disparate computing systemplatforms, architectures, and data storage devices, and collaborationbetween multiple users or agents. Further, a collaborative dataset mayalso be associated with data configured to establish one or moreassociations (e.g., metadata) among subsets of dataset attribute datafor datasets, whereby attribute data, such as dataset attributes, may beused to determine correlations (e.g., data patterns, trends, rankingsper unit time, etc.) among the collaborative datasets. Collaborativedatasets, with or without associated dataset attribute data, may be usedto facilitate easier collaborative dataset interoperability amongsources of data, which may be formatted differently at origination, ormay be disposed at disparate data stores (e.g., repositories atdifferent geographical locations). In some examples, the term“collaborative dataset” may be used interchangeably with “consolidateddataset.”

Dataset creation interface 102 may be used to create, or initiatecreation of, a collaborative dataset via computing device 109 a, whichmay be associated with a user 108 a. As shown, dataset creationinterface 102 includes a number of user interface elements to facilitatedataset creation, such as a search field 121, a dataset descriptionfield 103, a file upload interface 106, a create dataset activationinput 141, and any other type of user interface element that may be usedto create a dataset that, in turn, may be transformed into atomizeddatasets, such as atomized dataset 142 a stored in repository 140.According to various examples, user interface elements may constitute asubset of one or more structures and/or functions of computerized toolsdescribed herein. “User interface element” may refer to, at least insome examples, a subset of executable instructions that interfaces orinteracts with one or more applications or programs to initiate,facilitate, and/or perform execution of instructions in accordance withvarious implementations set forth herein. In some cases, at least oneuser interface element and at least one subset of executableinstructions (e.g., in applications, modules, software components, etc.)may interoperate in combination to set forth one or more specializedfunctions or structures described herein.

To illustrate operation of dataset creation interface 102, consider thatuser 108 a via computing device 109 a initiates a computer-based actionin which a user interface element representing a file 105 is selectedand dragged via pointer element 107 (e.g., a pointer device, or anyother interface selection tool, including a finger) into file uploadinterface 106. Computing device 109 a may detect a data signal generatedby the implementation of “create dataset” input 141, which may initiatecreation of the dataset. As an example, consider that file 105 mayinclude data formatted in a particular data arrangement, such asformatted as a CSV file, a TSV, an XLS file, or the like. In oneexample, a set of data 104 from file 105 may be uploaded, responsive todragging icon of file 105 to upload interface 106, into collaborativedataset consolidation system 110, which, in turn, may generate anatomized dataset 142 a. An atomized dataset 142 a may include a dataarrangement in which data is stored as an atomized data point 114 that,for example, may be an irreducible or simplest representation of datathat may be linkable to other atomized data points, according to someembodiments. Note that in some examples, atomized dataset 142 a may belinked (e.g., during the dataset creation process) via links 111 toother datasets, such as public datasets 113 a and 113 b, to form acollaborative dataset. Public datasets 113 a and 113 b may originateexternal to collaborative dataset consolidation system 110, such as atcomputing device 102 a and computing device 102 b, respectively, Users101 a and 101 b are shown to be associated with computing devices 102 aand 102 b, respectively.

Logic in computing device 109 a or collaborative dataset consolidationsystem 110, or both, may be configured to identify and/or deriveattributes (e.g., dataset attributes) of the collaborative dataset,whereby logic may be implemented in hardware, software, or a combinationthereof. In some cases, dataset attributes may be identified in the textdescription of the information entered into dataset description field103. In some cases, the logic may implement natural language processing,or the like, to parse through strings of text to identify key words forimplementation as dataset attributes. In other cases, collaborativedataset consolidation system 110 may identify or derive attributes basedon annotations or other information related to set of data 104 (e.g.,annotations based on column header data, etc.).

In some embodiments, collaborative dataset consolidation system 110 mayprovide access limited to the dataset attributes associated with thecollaborative dataset rather than the data (e.g., atomized data points114). Therefore, user 108 a may enter search terms into the search field121 to search for any relevant datasets that may augment or otherwisesupplement a current collaborative dataset. To illustrate, consider thata search via field 121 identifies dataset 113 n as having relevant dataattributes. Note, however, that dataset 113 n is shown as a “privatedataset” that includes protected data 131 c. Access to dataset 113 n maybe permitted via computing device 102 n by administrative user 101 n.Therefore, user 108 a via computing device 109 a may initiate a requestto access protected data 131 c through secured link 119 upon activationof user input (“link”) 143, or by providing authorized credential datato retrieve data via secured link 119. Collaborative dataset 142 a thenmay be supplemented by linking to protected data 131 c to form a largeratomized dataset that includes data from datasets 142 a, 113 a, 113 b,and 113 n. According to various examples, a “private dataset” may haveone or more levels of security. For example, a private dataset as wellas metadata describing the private dataset may be entirely inaccessibleby non-authorized users of collaborative dataset consolidation system110. Thus, a private dataset may be shielded or invisible to searchesperformed via search field 121. In another example, a private datasetmay be classified as “restricted,” or inaccessible (e.g., withoutauthorization), whereby its associated metadata describing datasetattributes of the private dataset may be accessible so the dataset maybe discovered via search field 121 or identified by any other mechanism.A restricted dataset may be accessed via authorization credentials,according to some examples.

A second example of a computerized tool is shown implemented ascollaborative dataset access interface 122, which may be configured toaccess or otherwise query a collaborative database through a data entryinterface 124. According to some examples, data entry interface 124 maybe configured to accept commands (e.g., queries) in high-level languages(e.g., high-level programming languages, including object-orientedlanguages, etc.), such as in Python™ and structured query language(“SQL”), among others. Further, commands in a high-level language may beconverted into a graph-level access or query language, such as SPARQL orthe like. Thus, a query may be initiated at computing device 109 a viauser interface 122 to query data associated with the atomized dataset(e.g., data from datasets 142 a, 113 a, 113 b, and 113 n). In someexamples, data entry interface 124 may be configured to acceptprogramming languages for facilitating other data operations, such asstatistical and data analysis. Examples of programming languages toperform statistical and data analysis include “R,” which is maintainedand controlled by “The R Foundation for Statistical Computing” atwww(dot)r-project(dot)org, as well as other like languages or packages,including applications that may be integrated with R (e.g., such asMATLAB™, Mathematica™ etc.).

A third example of a computerized tool is shown as collaborativeactivity interface 132, which may be configured to facilitatecollaboration of dataset 142 a among other datasets and among otherusers. In some examples, collaborative dataset consolidation system 110may determine correlations among datasets and dataset interactions,whereby the correlations may be fed via a dataset activity feed 134 todisseminate dataset-related information via computing device 109 a touser 108 a, as well as via other computing devices 102 a, 102 b, and 102n to other users 101 a, 101 b, and 101 n. Examples of notificationspresented in dataset activity feed 134 may include informationdescribing dataset interactions relating to an event in which aparticular dataset (e.g., a relevant dataset of interest) has beenqueried, modified, shared, accessed, created, etc., or an event in whichanother user commented on a dataset or received a comment for a dataset,etc. For example, a user, such as user 101 b, may post comments andnotes electronically to a user account of user 108 a, as a contributor(e.g., “Hi user 108 a. This is user 101 b—I noticed that a value ismissing in column XX.” Would you like me to correct this as acontributor?). User 101 b may activate a user input (not shown) togenerate a “like” data signal or a “bookmarked” data signal inassociation with user's 108 a dataset 142 a. The “like” data signals maycause a notification to be generated for presentation in datasetactivity feed 134 (e.g., “User 101 b *likes* your dataset 142 a”).Similarly, “bookmarked” data signals may cause another notification tobe generated for presentation in dataset activity feed 134 (e.g., “User101 b *has bookmarked* and saved a link to your dataset 142 a”).

Dataset activity feed 134 may present information describing trendingdataset and/or user information, whereby a particular subset of datasets(or dataset users) may be of predominant interest among a communityduring a period of time. Users of predominant interest may be indicatedby relatively high rankings, relatively high numbers of comments, anumber of “likes,” etc. As shown, dataset collaboration may be initiatedin a collaboration request portion 136 of interface 132, wherebyactivation of user input (“collaborate”) 135 may facilitate sharing ofdatasets or commentary among datasets. For example, user input 135 maybe activated to “add a contributor,” who may be invited to assist orcollaborate in data collection and analysis. User 108 a may grantcertain levels of access or permissions (e.g., “view only” permission,“view and edit” permission, etc.) In the event a certain dataset isprotected, then user 108 a may request access upon activation of input(“link”) 137 in dataset access request portion 138. User interfaceelement 139, when selected, may generate a request to seek authorizationto access the particular dataset. Thus, a community of users 108 a, 101a, 101 b, and 101 n, as well as any other participating user, maydiscover and share dataset-related information in real-time (orsubstantially in real-time) in association with collaborative datasets.According to various embodiments, one or more structural and/orfunctional elements described in FIG. 1, as well as below, may beimplemented in hardware or software, or both.

In view of the foregoing, the structures and/or functionalities depictedin FIG. 1 illustrate computerized tools configured to discover, form,and analyze, for example, via one or more user interface applications,collaborative datasets and interrelations among a system of networkedcollaborative datasets, according to some embodiments. User interfaces,and user interface elements therein, may be configured to createcollaborative datasets by, for example, causing datasets to linkautomatically to other datasets. For example, collaborative datasets maybe formed by “suggesting” similar or related compatible datasets uponingesting a dataset and building a model of its metadata or schema. Invarious examples, creation of a dataset may including forming linksamong atomized datasets, whereby at least some links can be formed viagraph data (e.g., at levels at which graph data arrangements are storedin, for example, graph databases). According to some embodiments, graphdata arrangements may facilitate connecting and relating increasingamounts of data relative to other data storage technologies that may berelatively inflexible in adapting to increased amounts of data (e.g.,increases in relatively large amounts of data). Also, user interfacesand user interface elements may be configured to provide varying levelsof access to one or more datasets. For example, a user interface as acomputerized tool may be configured to supplement a collaborativedataset by linking, for example, to protected data 131 c to form alarger atomized dataset including data from datasets 142 a, 113 a, 113b, and 113 n.

Further, the structures and/or functionalities depicted in FIG. 1illustrate computerized tools configured to establish and evaluatewhether a particular dataset may be useful or satisfactory in, forexample, forming a collaborative dataset that may be used to form datamodels. The data models may be used to analyze datasets to provetheories set forth by data scientists, statisticians, datapractitioners, and the like. In one example, dataset creation interface102 may be configured to initiate creation of a dataset during which“insight” information may be generated. During dataset creation, a setof data or a dataset may be optionally normalized by, for example,forming a hashed representation of the contents of a file (i.e., areduced or compressed representation of the data file), whereby a hashvalue may be used for content addressing.

“Insight information” may refer, in some examples, to information thatmay automatically convey (e.g., visually in text and/or graphics)dataset attributes of a created dataset, including derived datasetattributes, during or after (e.g., shortly thereafter) the creation ofthe dataset. In some examples, a user need not further manipulate thedata by applying, for example, statistical algorithms against thecreated dataset to view insight information. Insight informationpresented in a user interface (e.g., responsive to dataset creation) maydescribe various aspects of a dataset, in summary form, such as, but notlimited to, annotations (e.g., of columns, cells, or any portion ofdata), data classifications (e.g., a geographical location, such as azip code, etc.), datatypes (e.g., string, numeric, categorical, Boolean,integer, etc.), a number of data points, a number of columns, a “shape”or distribution of data and/or data values, a number of empty ornon-empty cells in a tabular data structure, a number of non-conformingdata (e.g., a non-numeric data value in column expecting a numeric data,an image file, etc.) in cells of a tabular data structure, a number ofdistinct values, etc. According to some embodiments, initiation of thedataset creation process invoked at user input 141 may also performstatistical data analysis during or upon the creation of the dataset.For example, logic disposed in collaborative dataset consolidationsystem 110 or at a client computing device, or both, may be configuredto determine statistical characteristics as dataset attributes of alinked collaborative dataset. For instance, the logic can be configuredto calculate a mean of the dataset distribution, a minimum value,maximum value, a value of standard deviation, a value of skewness, avalue of kurtosis, etc., among any type of statistic or characteristic.As such, a user, when determining whether to use a dataset, need notdownload a dataset to perform ad hoc data analysis (e.g., creating andrunning a Python script against downloaded data to perform a statisticalanalysis, or the like) to identify characteristics of a distribution ofdata as well as visualization of the distribution.

Additionally, the structures and/or functionalities depicted in FIG. 1illustrate computerized tools configured to identify interactions amonga set of any number of datasets that may include user datasets, a groupof other user datasets, a group of non-user datasets (e.g., datasetsexternal to collaborative dataset consolidation system 110), and thelike. Correlations among datasets and dataset interactions may becalculated and summarized for presentation via, for example, datasetactivity feed 134 to provide a user 108 a with dataset-relatedinformation, such as whether a particular dataset of has been queried,modified, shared, accessed, created, etc., or an event in which anotheruser commented on a dataset or received a comment for a dataset.Therefore, data practitioners may gain additional insights into whethera particular dataset may be relevant based on electronic socialinteractions among datasets and users. For example, a dataset may beassociated with a rating (e.g., a number between 1 to 10, as aggregatedamong numeric rankings voted upon by other users), whereby the ratingmay be indicative of the “applicability” or “quality” of the dataset.Other examples may include data representations via dataset activityfeed 134 that conveys a number of queries associated with a dataset, anumber of dataset versions, identities of users (or associated useridentifiers) who have analyzed a dataset, a number of user commentsrelated to a dataset, the types of comments, etc.). Thus, at least someimplementations described herein may provide for “a network fordatasets” (e.g., a “social” network of datasets and datasetinteractions). While “a network for datasets” need not be based onelectronic social interactions among users, various examples provide forinclusion of users and user interactions (e.g., social network of datapractitioners, etc.) to supplement the “network of datasets.”Collaboration among users and formation of collaborative datasetstherefore may expedite dataset analysis and hypothesis testing based onup-to-date information provided by dataset activity feed 134, whereby auser may more readily determine applicability of a dataset to modelingdata and/or proving a theory.

FIG. 2 is a diagram depicting an example of programmatic interface,according to some examples. Diagram 200 depicts an example of aprogrammatic interface 202 that may be configured to facilitate dataexchange and execution of instructions among any number of applicationsdisposed in either one or more client computing devices 209 or one ormore server computing devices 219, or any combination thereof.Programmatic interface 202 may include one or more subsets of executablecode and, optionally, one or more processors for performing any numberof functions by executing the executable code. Programmatic interface202 may be configured to facilitate data communications and executionover any number of processors and data stores (e.g., hardware), and mayprovide for execution of instructions at either computing device 209 orcomputing device 219, or over both devices 209 and 219 via network 201.Thus, programmatic interface 202 may facilitate performance of any ofone or more functions described herein at either client computing device209 or server computing device 219, as well as facilitatingcollaborative computing via data 229 exchanges over network 201 betweenclient computing device 209 and server computing device 219.

Further to diagram 200, client-side executable code 220 may beimplemented in association with computing device 209, and may include,for example, a browser application 222, one or more APIs 224, and anyother programmatic code 226 for performing functions described herein.Also shown in diagram 200, server-side executable code 230 may include,for example, a web server application 232, one or more APIs 234, and anyother programmatic code 236 for performing functions described herein.To illustrate a subset of operations of programmatic interface 202,consider that data (e.g., raw data in sets of data) may be transmittedover network 201 (e.g., from server computing device 219 or any othersource of data) to computing device 209 at which dataset creation (orany other function described herein, such as insight generation) may beinitiated and performed by executing client-side executable code 220. Asanother example, consider that execution of client-side executable code220 may cause data to be transferred to server computing device 219 fromclient computing device 209 or any other source of data. Server-sideexecutable code 230 may be executed to create, for example, datasets andinsight information, and to provide access to the created datasets andinsight information via data exchanges 229 to client computing device209. Note that these examples are not limiting and that any function (orconstituent portion thereof) may be performed at any subset ofexecutable instructions disposed at one or more of computing devices 209and 219.

According to some examples, programmatic interface 202 may facilitatedata communication and interaction, including instruction execution,among one or more similar or different computer hardware platforms, oneor more similar or different operating systems, one or more similar ordifferent programming languages and levels thereof (e.g., fromhigh-level to low-level programming languages), one or more similar ordifferent processes, procedures, and objects, one or more similar ordifferent protocols, and the like. According to some examples,programmatic interface 202 (or a portion thereof) may be implemented asan application programming interface, or “API,” or as any number ofAPIs.

FIG. 3 is a diagram depicting a flow diagram as an example ofcollaborative dataset creation, according to some embodiments. Flow 300may be an example of initiating creation of the dataset, such as acollaborative dataset, based on a set of data. In some examples, flow300 may be implemented in association with a user interface. At 302,data to form an input as a user interface element may be received via auser interface. For example, a processor executing instruction data at aclient computing device (or any other type of computing device,including a server computing device) may receive data to form a userinterface element, which may constitute a user input that may bepresented in a user interface as, for example, a “create dataset” userinput. In one or more cases, activation of the “create dataset” userinput can initiate creation of an atomized dataset based on a set ofdata, which may include, for example, raw data in data file (e.g., atabular data file, such as a XLS file, etc.). According to someexamples, receiving data to form “create dataset” user input may besubsequent to receiving data to form another input (as another userinterface element). In some examples, this other user input that may bepresented in a user interface as, for example, a “upload” user inputthat is configured perform an upload of the set of data from a datasource (e.g., external, third-party data source, which may or may not bepublic). In at least one case, activation of the “upload” user input mayinitiate transmission of an upload instruction to a server computingsystem (e.g., implemented as a collaborative dataset consolidationsystem) to import the set of data prior to the data creation process.

At 304, a programmatic interface may be activated to facilitate thecreation of the dataset responsive to receiving the first input. Theprogrammatic interface may be implemented as either hardware orsoftware, or a combination thereof. The programmatic interface also maybe disposed at a client computing device or a server computing device,which may be associated with a collaborative dataset consolidationsystem, or may distributed over any number of computing devices whethernetworked together or otherwise. In some examples, the programmaticinterface may be distributed as subsets of executable code (e.g., asscripts, etc.) to implement APIs in any number of computing devices. Insome embodiments, programmatic interface may be optional and may beomitted.

At 306, a set of data may be transformed from a first format to anatomized format to form an atomized dataset. The atomized dataset may bestored in a graph data structure, according to some examples. In variousexamples, the transformation into an atomized dataset may be performedat a client computing device, a server computing device, or acombination of multiple computing devices. According to variousembodiments, the transformation of formats from one to another formatmay be performed at any process or computing device. For example, thetransformation (or portion thereof) may be performed at either a clientcomputing device or a server computing device, or both (e.g.,distributed computing).

At 308, the creation of the dataset may be monitored by, for example, aprocessor. In various examples, the creation of a dataset may passthrough one or more phases. In one phase, for example, the data may becleaned (e.g., data entry exceptions, such as defective data, may bedetected and corrected). In another phase, insight informationdescribing the dataset and its attributes may be generated. And in yetanother phase, the dataset may be transformed and linked to otheratomized datasets to form a collaborative dataset. One or more of thesephases may be visually depicted using user interface elements (e.g., aprogress bar or the like) upon commencement of the dataset creationprocess. Additional user interface elements, such as a “link” user input137 of FIG. 1, may be presented on a user interface to facilitatelinking of atomized datasets (e.g., responsive to input insightinformation, dataset activity feed information, etc.). At 310, datarepresenting a status of at least a portion of the creation of thedataset may be presented on the user interface. The status may depictthat the atomized dataset is linked to at least one other dataset.

In one embodiment, the monitoring of dataset creation at 308 may includeidentifying data to form an insight user interface element. The userinterface element may be configured to specify a status of an insightphase for a dataset creation process. During the insight phase, datarepresenting insights of the set of data may be formed, whereby insightinformation may specify at least one dataset attribute, such asannotations, datatypes, inferred dataset attributes, etc. Further, themonitoring of dataset creation at 308 may include identifying data toform a linking user interface element to specify the status of a linkingphase of the dataset creation process. During the linking phase, datarepresenting formation of a link among the atomized dataset and otherdatasets, which includes at least one other atomized dataset, may bepresented to a user interface. Accordingly, at 310, an insight userinterface element and a linking user interface element may be presentedat the user interface. Note that any of 302, 304, 306, 308, and 310 maybe performed at any process or computing device, and may be performed ateither a client computing device or a server computing device, or both(e.g., distributed computing).

In another embodiment, a number of notifications may be implemented as asubset of user interface elements that constitute a dataset activityfeed. A notification may specify type of dataset interaction that may becharacterized for a particular dataset or user. Examples of datasetinteractions may include data specifying one or more of the following: anew dataset is created, a dataset is queried, a dataset is linked toanother dataset, a comment relating to a dataset is associated thereto,a dataset is relevant to another dataset, and other like characterizeddataset interactions. The characterization of a dataset interaction maybe performed during monitoring of the dataset creation process at 308. Acharacterized dataset interaction may be presented as a status of thedataset at 310 as a notification in an activity feed. Moreover, a userinput interface element associated with a characterized datasetinteraction may be configured to initiate access to a dataset for whichthe dataset interaction is characterized (e.g., the characterizeddataset interaction may be a modified dataset). Therefore, a user, suchas a data practitioner, may interact with one or more user interfaceelements based on the characterized datasets interactions, which provideinformation that may be useful to determine whether to use, or to linkto, that dataset to form a collaborative dataset. For example, datapractitioner interested in gun violence statistics may be interested inlearning about, via a dataset activity feed, updates or queries relatingto certain law enforcement databases.

According to some examples, characterized datasets interactions mayspecify relevant dataset data or relevant collaborator data (i.e.,relative to a particular user's dataset or user characteristics).Relevant dataset data may specify a subset of datasets having datasetattributes calculated to be relevant to a particular atomized dataset,whereas relevant collaborator data may specify a subset of user accountshaving user account attributes calculated to be relevant to a useraccount associated with the atomized dataset. According to someadditional examples, characterized datasets interactions may specifytrending dataset data or trending collaborator data (i.e., relative to acommunity of datasets or users). Trending dataset data may specify asubset of datasets having dataset attributes calculated to includegreater dataset attribute values relative to a superset of datasets. Forexample, trending dataset data, such as a total number of queries perunit time, may specify, for example, a list of “top ten” datasetsrelating to a particular topic or discipline (e.g., “top ten” datasetsbased on locations and cases of Zika virus infections).

Trending collaborator data may specify a subset of user accounts havinguser account attributes calculated to include greater user accountattributes values relative to a superset of user accounts. For example,trending collaborator data, such as users that have datasets with agreatest number of comments per unit time, may specify, for example, alist of “top ten” collaborators relating to the particular topic ordiscipline. In some examples, “trend” related information may describechanges (e.g., statistical changes) in, or general movement of, data orinformation about datasets over time to predict or estimate patterns ofdataset interactions and usage. In some cases, trend-related informationmay include ranking data (e.g., rankings of dataset attributes or userattributes) over unit time.

FIG. 4 is a diagram depicting a collaborative dataset consolidationsystem, according to some embodiments. Diagram 400 depicts an example ofcollaborative dataset consolidation system 410 that may be configured toconsolidate one or more datasets to form collaborative datasets. Acollaborative dataset, according to some non-limiting examples, is a setof data that may be configured to facilitate data interoperability overdisparate computing system platforms, architectures, and data storagedevices. Further, a collaborative dataset may also be associated withdata configured to establish one or more associations (e.g., metadata)among subsets of dataset attribute data for datasets, whereby attributedata may be used to determine correlations (e.g., data patterns, trends,etc.) among the collaborative datasets. Collaborative datasetconsolidation system 410 may present the correlations via computingdevices 409 a and 409 b to disseminate dataset-related information toone or more users 408 a and 408 b. Thus, a community of users 408, aswell as any other participating user, may discover and sharedataset-related information of interest in association withcollaborative datasets. Collaborative datasets, with or withoutassociated dataset attribute data, may be used to facilitate easiercollaborative dataset interoperability among sources of data that may bedifferently formatted at origination or may be disposed at disparatedata stores (e.g., repositories at different geographical locations).According to various embodiments, one or more structural and/orfunctional elements described in FIG. 4, as well as below, may beimplemented in hardware or software, or both.

Collaborative dataset consolidation system 410 is depicted as includinga dataset ingestion controller 420, a dataset query engine 430, acollaboration manager 460, a collaborative data repository 462, and adata repository 440, according to the example shown. Dataset ingestioncontroller 420 may be configured to receive data representing a dataset404 a having, for example, a particular data format (e.g., CSV, XML,JSON, XLS, MySQL, binary, etc.), and may be further configured toconvert dataset 404 a into a collaborative data format for storage in aportion of data arrangement 442 a in repository 440. According to someembodiments, a collaborative data format may be configured to, but neednot be required to, format data in converted dataset 404 a as anatomized dataset. An atomized dataset may include a data arrangement inwhich data is stored as an atomized data point 414 that, for example,may be an irreducible or simplest representation of data that may belinkable to other atomized data points, according to some embodiments.Atomized data point 414 may be implemented as a triple or any other datarelationship that expresses or implements, for example, a smallestirreducible representation for a binary relationship between two dataunits. As atomized data points may be linked to each other, dataarrangement 442 a may be represented as a graph, whereby the converteddataset 404 a (i.e., atomized dataset 404 a) forms a portion of thegraph. Atomized data point 414, in some cases, may be expressed in astatement in which one object or entity relates (or links) to anotherobject or entity, whereby the objects and the relationship (e.g., thelink) each may be individually addressable. In some cases, an atomizeddataset facilitates merging of data irrespective of whether, forexample, schemas or applications differ.

Further, dataset ingestion controller 420 may be configured to identifyother datasets that may be relevant to dataset 404 a. In oneimplementation, dataset ingestion controller 420 may be configured toidentify associations, links, references (e.g., annotations, etc.),pointers, etc. that may indicate, for example, similar subject matterbetween dataset 404 a and a subset of other datasets (e.g., within orwithout repository 440). In some examples, dataset ingestion controller420 may be configured to correlate dataset attributes of an atomizeddataset with other atomized datasets or non-atomized datasets. Further,dataset ingestion controller 420 also may be configured to correlatedataset attributes of any public data (or atomized dataset) to any otherpublic data (or other atomized datasets), whereby public data anddatasets may be accessible (e.g., without credentials). In someexamples, dataset ingestion controller 420 may be configured tocorrelate dataset attributes of private data (or private atomizeddataset) to other data (or other atomized datasets), whereby the data inprivate data and atomized datasets may be accessible, for example, withauthorized credentials. Dataset ingestion controller 420 or other anyother component of collaborative dataset consolidation system 410 may beconfigured to format or convert a non-atomized dataset (or any otherdifferently-formatted dataset) into a format similar to that ofconverted dataset 404 a). Therefore, dataset ingestion controller 420may determine or otherwise use associations to identify datasets withwhich to consolidate to form, for example, collaborative datasets 432 aand collaborative datasets 432 b. Thus, dataset ingestion controller 420may be configured to identify correlated dataset attributes for“discovery purposes.” That is, correlated dataset attributes (or otherrepresentations thereof, such as annotations) may be made “searchable,”whereby any user or participant may search for an attribute and receivesearch results indicating relevant public or private (i.e., protected)datasets. Note that while dataset ingestion controller 420 may makecorrelated dataset attributes from private datasets accessible,authorization may be required to access or perform any operation on theprivate datasets correlated by dataset ingestion controller 420.

As shown in diagram 400, dataset ingestion controller 420 may beconfigured to extend a dataset (i.e., the converted dataset 404 a storedin data arrangement 442 a) to include, reference, combine, orconsolidate with other datasets within data arrangement 442 a orexternal thereto. Specifically, dataset ingestion controller 420 mayextend an atomized dataset 404 a to form a larger or enriched dataset,by associating or linking (e.g., via links 411) to other datasets, suchas external entity datasets 404 b, 404 c, and 404 n, form one or morecollaborative datasets. Note that external entity datasets 404 b, 404 c,and 404 n may be converted (or convertible) to form external datasetsatomized datasets 442 b, 442 c, and 442 n, respectively. The term“external dataset,” at least in this case, can refer to a datasetgenerated externally to system 410 and may or may not be formatted as anatomized dataset.

As shown, different entities 405 a, 405 b, and 405 n may each include acomputing device 402 (e.g., representative of one or more servers and/ordata processors) and one or more data storage devices 403 (e.g.,representative of one or more database and/or data store technologies).Examples of entities 405 a, 405 b, and 405 n include individuals, suchas data scientists and statisticians, corporations, universities,governments, etc. A user 401 a, 401 b, and 401 n (and associated useraccount identifiers) may interact with entities 405 a, 405 b, and 405 n,respectively. Each of entities 405 a, 405 b, and 405 n may be configuredto perform one or more of the following: generating datasets, searchingdata and/or data attributes of datasets, discovering datasets, linkingto datasets (e.g., public and/or private datasets), modifying datasets,querying datasets, analyzing datasets, hosting datasets, and the like,whereby one or more entity datasets 404 b, 404 c, and 404 n may beformatted in different data formats. In some cases, these formats may beincompatible for implementation with data stored in repository 440. Asshown, differently-formatted datasets 404 b, 404 c, and 404 n may beconverted into atomized datasets, each of which is depicted in diagram400 as being disposed in a dataspace. Namely, atomized datasets 442 b,442 c, and 442 n are depicted as residing in dataspaces 413 a, 413 b,and 413 n, respectively. In some examples, atomized datasets 442 b, 442c, and 442 n may be represented as graphs.

According to some embodiments, atomized datasets 442 b, 442 c, and 442 nmay be imported into collaborative dataset consolidation system 410 forstorage in one or more repositories 440. In this case, dataset ingestioncontroller 420 may be configured to receive entity datasets 404 b, 404c, and 404 n for conversion into atomized datasets, as depicted incorresponding dataspaces 413 a, 413 b, and 413 n. Collaborative dataconsolidation system 410 may store atomized datasets 442 b, 442 c, and442 n in repository 440 (i.e., internal to system 410) or may providethe atomized datasets for storage in respective entities 405 a, 405 b,and 405 n (i.e., without or external to system 410). Alternatively, anyof entities 405 a, 405 b, and 405 n may be configured to convert entitydatasets 404 b, 404 c, and 404 n and store corresponding atomizeddatasets 442 b, 442 c, and 442 n in one or more data storage devices 403a, 403 b, and 430 c. In this case, atomized datasets 442 b, 442 c, and442 n may be hosted for access by dataset ingestion controller 420 forlinking via links 411 to extend datasets with data arrangement 442 a.

Thus, collaborative dataset consolidation system 410 is configured toconsolidate datasets from a variety of different sources and in avariety of different data formats to form collaborative datasets 432 aand 432 b. As shown, collaborative dataset 432 a extends a portion ofdataset in data arrangement 442 a to include portions of atomizeddatasets 442 b, 442 c, and 442 n via links 411, whereas collaborativedataset 432 b extends another portion of a dataset in data arrangement442 a to include other portions of atomized datasets 442 b and 442 c vialinks 411. Note that entity dataset 404 n includes a secured set ofprotected data 431 c that may require a level of authorization orauthentication to access. Without authorization, link 419 cannot beimplemented to access protected data 431 c. For example, user 401 n maybe a system administrator that may program computing device 402 n torequire authorization to gain access to protected data 431 c. In somecases, dataset ingestion controller 420 may or may not provide anindication that link 419 exists based on whether, for example, user 408a has authorization to form a collaborative dataset 432 b to includeprotected data 431 c. In some examples, user 401 n may permit access todataset attributes associated with protected data 431 c, whereby thedataset attributes may be accessed by collaborative datasetconsolidation system 410 to enable other users to search for anddiscover relevant dataset attributes for protected data 431 c.Thereafter, an interested user 401 a, 401 b, or 408 a may request accessto the protected data 431 c. Access may be granted for a limited time,for a limited purpose, for pecuniary or charitable reasons, or any otherpurpose or with any other limitation.

Dataset query engine 430 may be configured to generate one or morequeries, responsive to receiving data representing one or more queriesvia computing device 409 a from user 408 a. Dataset query engine 430 isconfigured to apply query data to one or more collaborative datasets,such as collaborative dataset 432 a and collaborative dataset 432 b, toaccess the data therein to generate query response data 412, which maybe presented via computing device 409 a to user 408 a. According to someexamples, dataset query engine 430 may be configured to identify one ormore collaborative datasets subject to a query to either facilitate anoptimized query or determine authorization to access one or more of thedatasets, or both. As to the latter, dataset query engine 430 may beconfigured to determine whether one of users 408 a and 408 b isauthorized to include protected data 431 c in a query of collaborativedataset 432 b, whereby the determination may be made at the time (orsubstantially at the time) dataset query engine 430 identifies one ormore datasets subject to a query.

Collaboration manager 460 may be configured to assign or identify one ormore attributes associated with a dataset, such as a collaborativedataset, and may be further configured to store dataset attributes ascollaborative data in repository 462. Examples of dataset attributesinclude, but are not limited to, data representing a user accountidentifier, a user identity (and associated user attributes, such as auser first name, a user last name, a user residential address, aphysical or physiological characteristics of a user, etc.), one or moreother datasets linked to a particular dataset, one or more other useraccount identifiers that may be associated with the one or moredatasets, data-related activities associated with a dataset (e.g.,identity of a user account identifier associated with creating,searching, discovering, analyzing, discussing, collaborating with,modifying, querying, etc. a particular dataset), and other similarattributes. Another example of a dataset attribute is a “usage” or typeof usage associated with a dataset. For instance, a virus-relateddataset (e.g., Zika dataset) may have an attribute describing usage tounderstand victim characteristics (i.e., to determine a level ofsusceptibility), an attribute describing usage to identify a vaccine, anattribute describing usage to determine an evolutionary history ororigination of the Zika, SARS, MERS, HIV, or other viruses, etc.Further, collaboration manager 460 may be configured to monitor updatesto dataset attributes to disseminate the updates to a community ofnetworked users or participants. Therefore, users 408 a and 408 b, aswell as any other user or authorized participant, may receivecommunications (e.g., via user interface) to discover new orrecently-modified dataset-related information in real-time (or nearreal-time).

In view of the foregoing, the structures and/or functionalities depictedin FIG. 4 illustrate a dataset consolidated system that may beconfigured to consolidate datasets originating in different data formatswith different data technologies, whereby the datasets (e.g., ascollaborative datasets) may originate external to the system.Collaborative dataset consolidation system 410, therefore, may beconfigured to extend a dataset beyond its initial quantity and quality(e.g., types of data, etc.) of data to include data from other datasets(e.g., atomized datasets) linked to the dataset to form a collaborativedataset. Note that while a collaborative dataset may be configured topersist in repository 440 as a contiguous dataset, collaborative datasetconsolidation system 410 is configured to store at least one of atomizeddatasets 442 a, 442 b, 442 c, and 442 n (e.g., one or more of atomizeddatasets 442 a, 442 b, 442 c, and 442 n may be stored internally orexternally) as well data representing links 411. Hence, at a given pointin time (e.g., during a query), the data associated one of atomizeddatasets 442 a, 442 b, 442 c, and 442 n may be loaded into an atomicdata store against which the query can be performed. Therefore,collaborative dataset consolidation system 410 need not be required togenerate massive graphs based on numerous datasets, but rather,collaborative dataset consolidation system 410 may create a graph basedon a collaborative dataset in one operational state (of a number ofoperational states), and can be partitioned in another operational state(but can be linked via links 411 to form the graph). In some cases,different graph portions may persist separately and may be linkedtogether when loaded into a data store to provide resources for a query.Further, collaborative dataset consolidation system 410 may beconfigured to extend a dataset beyond its initial quantity and qualityof data based on using atomized datasets that include atomized datapoints (e.g., as an addressable data unit or fact), which facilitateslinking, joining, or merging the data from disparate data formats ordata technologies (e.g., different schemas or applications for which adataset is formatted). Atomized datasets facilitate datainteroperability over disparate computing system platforms,architectures, and data storage devices, according to variousembodiments.

According to some embodiments, collaborative dataset consolidationsystem 410 may be configured to provide a granular level of securitywith which an access to each dataset is determined on adataset-by-dataset basis (e.g., per-user access or per-user accountidentifier to establish per-dataset authorization). Therefore, a usermay be required to have per-dataset authorization to access a group ofdatasets less than a total number of datasets (including a singledataset). In some examples, dataset query engine 430 may be configuredto assert access-level (e.g., query-level) authorization orauthentication. Note that authorization or credentials may be embeddedin or otherwise associated with, for example, addresses referencingindividual entities (e.g., via an IRI, or the like). As such, non-users(e.g., participants) without account identifiers (or users withoutauthentication) may access or apply a query (e.g., limited to a query,for example) to repository 440 without receiving authorization to accesssystem 410 generally. Dataset query engine 430 may implement such aquery if, for example, the query includes, or is otherwise associatedwith, authorization data.

Collaboration manager 460 may be configured as, or to implement, acollaborative data layer and associated logic to implement collaborativedatasets for facilitating collaboration among consumers of datasets. Forexample, collaboration manager 460 may be configured to establish one ormore associations (e.g., as metadata) among dataset attribute data (fora dataset) and/or other attribute data (for other datasets (e.g., withinor without system 410)). As such, collaboration manager 460 candetermine a correlation between data of one dataset to a subset of otherdatasets. In some cases, collaboration manager 460 may identify andpromote a newly-discovered correlation to users associated with a subsetof other databases. Or, collaboration manager 460 may disseminateinformation about activities (e.g., name of a user performing a query,types of data operations performed on a dataset, modifications to adataset, etc.) for a particular dataset. To illustrate, consider thatuser 408 a is situated in South America and is accessing arecently-generated dataset (e.g., to analyze, query, etc.), therecently-generated dataset including data about the Zika virus overdifferent age ranges and genders over various population ranges.Further, consider that user 408 b is situated in North America and alsohas generated or curated datasets directed to the Zika virus.Collaborative dataset consolidation system 410 may be configured todetermine a correlation between the datasets of users 408 a and 408 b(i.e., subsets of data may be classified or annotated as Zika-related).System 410 also may optionally determine whether user 408 b hasinteracted with the newly-generated dataset about the Zika virus(whether the user, for example, viewed, accessed, downloaded data from,analyzed, queried, searched, added data to, etc. the dataset).Regardless, collaboration manager 460 may generate a notification topresent in a user interface 418 of computing device 409 b. As shown,user 408 b is informed in an “activity feed” portion 416 of userinterface 418 that “Dataset X” has been queried and is recommended touser 408 b (e.g., based on the correlated scientific and researchinterests related to the Zika virus). User 408 b, in turn, may modifyDataset X to form Dataset XX, thereby enabling a community ofresearchers to expeditiously access datasets (e.g., previously-unknownor newly-formed datasets) as they are generated to facilitate scientificcollaborations, such as developing a vaccine for the Zika virus. Notethat users 401 a, 401 b, and 401 n may also receive similarnotifications or information, at least some of which present one or moreopportunities to collaborate and use, modify, and share datasets in a“viral” fashion. Therefore, collaboration manager 460 and/or otherportions of collaborative dataset consolidation system 410 may providecollaborative data and logic layers to implement a “social network” fordatasets.

FIG. 5A is a diagram depicting an example of an atomized data point,according to some embodiments. Diagram 500 depicts a portion 501 of anatomized dataset that includes an atomized data point 514. In someexamples, the atomized dataset is formed by converting a data formatinto a format associated with the atomized dataset. In some cases,portion 501 of the atomized dataset can describe a portion of a graphthat includes one or more subsets of linked data. Further to diagram500, one example of atomized data point 514 is shown as a datarepresentation 514 a, which may be represented by data representing twodata units 502 a and 502 b (e.g., objects) that may be associated viadata representing an association 504 with each other. One or moreelements of data representation 514 a may be configured to beindividually and uniquely identifiable (e.g., addressable), eitherlocally or globally in a namespace of any size. For example, elements ofdata representation 514 a may be identified by identifier data 590 a,590 b, and 590 c.

In some embodiments, atomized data point 514 a may be associated withancillary data 503 to implement one or more ancillary data functions.For example, consider that association 504 spans over a boundary betweenan internal dataset, which may include data unit 502 a, and an externaldataset (e.g., external to a collaboration dataset consolidation), whichmay include data unit 502 b. Ancillary data 503 may interrelate viarelationship 580 with one or more elements of atomized data point 514 asuch that when data operations regarding atomized data point 514 a areimplemented, ancillary data 503 may be contemporaneously (orsubstantially contemporaneously) accessed to influence or control a dataoperation. In one example, a data operation may be a query and ancillarydata 503 may include data representing authorization (e.g., credentialdata) to access atomized data point 514 a at a query-level dataoperation (e.g., at a query proxy during a query). Thus, atomized datapoint 514 a can be accessed if credential data related to ancillary data503 is valid (otherwise, a request to access atomized data point 514 a(e.g., for forming linked datasets, performing analysis, a query, or thelike) without authorization data may be rejected or invalidated).According to some embodiments, credential data (e.g., passcode data),which may or may not be encrypted, may be integrated into or otherwiseembedded in one or more of identifier data 590 a, 590 b, and 590 c.Ancillary data 503 may be disposed in other data portion of atomizeddata point 514 a, or may be linked (e.g., via a pointer) to a data vaultthat may contain data representing access permissions or credentials.

Atomized data point 514 a may be implemented in accordance with (or becompatible with) a Resource Description Framework (“RDF”) data model andspecification, according to some embodiments. An example of an RDF datamodel and specification is maintained by the World Wide Web Consortium(“W3C”), which is an international standards community of Memberorganizations. In some examples, atomized data point 514 a may beexpressed in accordance with Turtle (e.g., Terse RDF Triple Language),RDF/XML, N-Triples, N3, or other like RDF-related formats. As such, dataunit 502 a, association 504, and data unit 502 b may be referred to as a“subject,” “predicate,” and “object,” respectively, in a “triple” datapoint. In some examples, one or more of identifier data 590 a, 590 b,and 590 c may be implemented as, for example, a Uniform ResourceIdentifier (“URI”), the specification of which is maintained by theInternet Engineering Task Force (“IETF”). According to some examples,credential information (e.g., ancillary data 503) may be embedded in alink or a URI (or in a URL) or an Internationalized Resource Identifier(“IRI”) for purposes of authorizing data access and other dataprocesses. Therefore, an atomized data point 514 may be equivalent to atriple data point of the Resource Description Framework (“RDF”) datamodel and specification, according to some examples. Note that the term“atomized” may be used to describe a data point or a dataset composed ofdata points represented by a relatively small unit of data. As such, an“atomized” data point is not intended to be limited to a “triple” or tobe compliant with RDF; further, an “atomized” dataset is not intended tobe limited to RDF-based datasets or their variants. Also, an “atomized”data store is not intended to be limited to a “triplestore,” but theseterms are intended to be broader to encompass other equivalent datarepresentations.

Examples of triplestores suitable to store “triples” and atomizeddatasets (and portions thereof) include, but are not limited to, anytriplestore type architected to function as (or similar to) a BLAZEGRAPHtriplestore, which is developed by Systap, LLC of Washington, D.C.,U.S.A.), any triplestore type architected to function as (or similar to)a STARDOG triplestore, which is developed by Complexible, Inc. ofWashington, D.C., U.S.A.), any triplestore type architected to functionas (or similar to) a FUSEKI triplestore, which may be maintained by TheApache Software Foundation of Forest Hill, Md., U.S.A.), and the like.

FIG. 5B is a diagram depicting operation an example of a collaborativedataset consolidation system, according to some examples. Diagram 550includes a collaborative dataset consolidation system 510, which, inturn, includes a dataset ingestion controller 520, a collaborationmanager 560, a dataset query engine 530, and a repository 540, which mayrepresent one or more data stores. In the example shown, consider that auser 508 b, which is associated with a user account data 507, may beauthorized to access (via networked computing device 509 b)collaborative dataset consolidation system to create a dataset and toperform a query. User interface 518 a of computing device 509 b mayreceive a user input signal to activate the ingestion of a data file,such as a CSV formatted file (e.g., “XXX.csv”), to create a dataset(e.g., an atomized dataset stored in repository 540). Hence, datasetingestion controller 520 may receive data 521 a representing the CSVfile and may analyze the data to determine dataset attributes during,for example, a phase in which “insights” (e.g., statistics, datacharacterization, etc.) may be performed. Examples of dataset attributesinclude annotations, data classifications, data types, a number of datapoints, a number of columns, a “shape” or distribution of data and/ordata values, a normative rating (e.g., a number between 1 to 10 (e.g.,as provided by other users)) indicative of the “applicability” or“quality” of the dataset, a number of queries associated with a dataset,a number of dataset versions, identities of users (or associated useridentifiers) that analyzed a dataset, a number of user comments relatedto a dataset, etc.). Dataset ingestion controller 520 may also convertthe format of data file 521 a to an atomized data format to form datarepresenting an atomized dataset 521 b that may be stored as dataset 542a in repository 540.

As part of its processing, dataset ingestion controller 520 maydetermine that an unspecified column of data 521 a, which includes five(5) integer digits, may be a column of “zip code” data. As such, datasetingestion controller 520 may be configured to derive a dataclassification or data type “zip code” with which each set of 5 digitscan be annotated or associated. Further to the example, consider thatdataset ingestion controller 520 may determine that, for example, basedon dataset attributes associated with data 521 a (e.g., zip code as anattribute), both a public dataset 542 b in external repositories 540 aand a private dataset 542 c in external repositories 540 b may bedetermined to be relevant to data file 521 a. Individuals 508 c, via anetworked computing system, may own, maintain, administer, host orperform other activities in association with public dataset 542 b.Individual 508 d, via a networked computing system, may also own,maintain, administer, and/or host private dataset 542 c, as well asrestrict access through a secured boundary 515 to permit authorizedusage. In some examples, either public dataset 542 b or private dataset542 c, or both, may be omitted (e.g., a user may select to exclude adataset, such as private dataset 542 c, from being inferred or otherwiselinked).

Continuing with the example, public dataset 542 b and private dataset542 c may include “zip code”-related data (i.e., data identified orannotated as zip codes). Dataset ingestion controller 520 may generate adata message 522 a that includes an indication that public dataset 542 band/or private dataset 542 c may be relevant to the pending uploadeddata file 521 a (e.g., datasets 542 b and 542 c include zip codes).Collaboration manager 560 receive data message 522 a, and, in turn, maygenerate user interface-related data 523 a to cause presentation of anotification and user input data configured to accept user input at userinterface 518 b. According to some examples, user 508 b may interact viacomputing device 509 b and user interface 518 b to (1) engage otherusers of collaborative dataset consolidation system 510 (and othernon-users), (2) invite others to interact with a dataset, (3) requestaccess to a dataset, (4) provide commentary on datasets viacollaboration manager 560, (5) provide query results based on types ofqueries (and characteristics of such queries), (6) communicate changesand updates to datasets that may be linked across any number of atomizeddataset that form a collaborative dataset, and (7) notify others of anyother type of collaborative activity relative to datasets.

If user 508 b wishes to “enrich” dataset 521 a, user 508 b may activatea user input (not shown on interface 518 b) to generate a user inputsignal data 523 b indicating a request to link to one or more otherdatasets, including private datasets that may require credentials foraccess. Collaboration manager 560 may receive user input signal data 523b, and, in turn, may generate instruction data 522 b to generate anassociation (or link 541 a) between atomized dataset 542 a and publicdataset 542 b to form a collaborative dataset, thereby extending thedataset of user 508 b to include knowledge embodied in externalrepositories 540 a. Therefore, user 508 b's dataset may be generated asa collaborative dataset as it may be based on the collaboration withpublic dataset 542 b, and, to some degree, its creators, individuals 508c. Note that while public dataset 542 b may be shown external to system510, public dataset 542 b may be ingested via dataset ingestioncontroller 520 for storage as another atomized dataset in repository540. Or, public dataset 542 b may be imported into system 510 as anatomized dataset in repository 540 (e.g., link 511 a is disposed withinsystem 510). Similarly, if user 508 b wishes to “enrich” atomizeddataset 521 b with private dataset 542 c, user 508 b may extend itsdataset 542 a by forming a link 511 b to private dataset 542 c to form acollaborative dataset. In particular, dataset 542 a and private dataset542 c may consolidate to form a collaborative dataset (e.g., dataset 542a and private dataset 542 c are linked to facilitate collaborationbetween users 508 b and 508 d). Note that access to private dataset 542c may require credential data 517 to permit authorization to passthrough secured boundary 515. Note, too, that while private dataset 542c may be shown external to system 510, private dataset 542 c may beingested via dataset ingestion controller 520 for storage as anotheratomized dataset in repository 540. Or, private dataset 542 c may beimported into system 510 as an atomized dataset in repository 540 (e.g.,link 511 b is disposed within system 510). According to some examples,credential data 517 may be required even if private dataset 542 c isstored in repository 540. Therefore, user 508 d may maintain dominion(e.g., ownership and control of access rights or privileges, etc.) of anatomized version of private dataset 542 c when stored in repository 540.

Should user 508 b desire not to link dataset 542 a with other datasets,then upon receiving user input signal data 523 b indicating the same,dataset ingestion controller 520 may store dataset 521 b as atomizeddataset 542 a without links (or without active links) to public dataset542 b or private dataset 542 c. Thereafter, user 508 b may enter querydata 524 a via data entry interface 519 (of user interface 518 c) todataset query engine 530, which may be configured to apply one or morequeries to dataset 542 a to receive query results 524 b. Note thatdataset ingestion controller 520 need not be limited to performing theabove-described function during creation of a dataset. Rather, datasetingestion controller 520 may continually (or substantially continuously)identify whether any relevant dataset is added or changed (beyond thecreation of dataset 542 a), and initiate a messaging service (e.g., viaan activity feed) to notify user 508 b of such events. According to someexamples, atomized dataset 542 a may be formed as triples compliant withan RDF specification, and repository 540 may be a database storagedevice formed as a “triplestore.” While dataset 542 a, public dataset542 b, and private dataset 542 c may be described above as separatelypartitioned graphs that may be linked to form collaborative datasets andgraphs (e.g., at query time, or during any other data operation,including data access), dataset 542 a may be integrated with eitherpublic dataset 542 b or private dataset 542 c, or both, to form aphysically contiguous data arrangement or graph (e.g., a unitary graphwithout links), according to at least one example.

FIG. 6 is a diagram depicting an example of a dataset analyzer and aninference engine, according to some embodiments. Diagram 600 includes adataset ingestion controller 620, which, in turn, includes a datasetanalyzer 630 and a format converter 640. As shown, dataset ingestioncontroller 620 may be configured to receive data file 601 a, which mayinclude a set of data (e.g., a dataset) formatted in any specificformat, examples of which include CSV, JSON, XML, XLS, MySQL, binary,RDF, or other similar or suitable data formats. Dataset analyzer 630 maybe configured to analyze data file 601 a to detect and resolve dataentry exceptions (e.g., whether a cell is empty or includes non-usefuldata, whether a cell includes non-conforming data, such as a string in acolumn that otherwise includes numbers, whether an image embedded in acell of a tabular file, whether there are any missing annotations orcolumn headers, etc.). Dataset analyzer 630 then may be configured tocorrect or otherwise compensate for such exceptions.

Dataset analyzer 630 also may be configured to classify subsets of data(e.g., each subset of data as a column) in data file 601 a as aparticular data classification, such as a particular data type. Forexample, a column of integers may be classified as “year data,” if theintegers are in one of a number of year formats expressed in accordancewith a Gregorian calendar schema. Thus, “year data” may be formed as aderived dataset attribute for the particular column. As another example,if a column includes a number of cells that each include five digits,dataset analyzer 630 also may be configured to classify the digits asconstituting a “zip code.” Dataset analyzer 630 can be configured toanalyze data file 601 a to note the exceptions in the processingpipeline, and to append, embed, associate, or link user interfaceelements or features to one or more elements of data file 601 a tofacilitate collaborative user interface functionality (e.g., at apresentation layer) with respect to a user interface. Further, datasetanalyzer 630 may be configured to analyze data file 601 a relative todataset-related data to determine correlations among dataset attributesof data file 601 a and other datasets 603 b (and attributes, such asmetadata 603 a). Once a subset of correlations has been determined, adataset formatted in data file 601 a (e.g., as an annotated tabular datafile, or as a CSV file) may be enriched, for example, by associatinglinks to the dataset of data file 601 a to form the dataset of data file601 b, which, in some cases, may have a similar data format as data file601 a (e.g., with data enhancements, corrections, and/or enrichments).Note that while format converter 640 may be configured to convert anyCSV, JSON, XML, XLS, RDF, etc. into RDF-related data formats, formatconverter 640 may also be configured to convert RDF and non-RDF dataformats into any of CSV, JSON, XML, XLS, MySQL, binary, XLS, RDF, etc.Note that the operations of dataset analyzer 630 and format converter640 may be configured to operate in any order serially as well as inparallel (or substantially in parallel). For example, dataset analyzer630 may analyze datasets to classify portions thereof, either prior toformat conversion by formatter converter 640 or subsequent to the formatconversion. In some cases, at least one portion of format conversion mayoccur during dataset analysis performed by dataset analyzer 630.

Format converter 640 may be configured to convert dataset of data file601 b into an atomized dataset 601 c, which, in turn, may be stored insystem repositories 640 a that may include one or more atomized datastore (e.g., including at least one triplestore). Examples offunctionalities to perform such conversions may include, but are notlimited to, CSV2RDF data applications to convert CVS datasets to RDFdatasets (e.g., as developed by Rensselaer Polytechnic Institute andreferenced by the World Wide Web Consortium (“W3C”)), R2RML dataapplications (e.g., to perform RDB to RDF conversion, as maintained bythe World Wide Web Consortium (“W3C”)), and the like.

As shown, dataset analyzer 630 may include an inference engine 632,which, in turn, may include a data classifier 634 and a datasetenrichment manager 636. Inference engine 632 may be configured toanalyze data in data file 601 a to identify tentative anomalies and toinfer corrective actions, and to identify tentative data enrichments(e.g., by joining with, or linking to, other datasets) to extend thedata beyond that which is in data file 601 a. Inference engine 632 mayreceive data from a variety of sources to facilitate operation ofinference engine 632 in inferring or interpreting a dataset attribute(e.g., as a derived attribute) based on the analyzed data. Responsive toa request input data via data signal 601 d, for example, a user mayenter a correct annotation via a user interface, which may transmitcorrective data 601 d as, for example, an annotation or column heading.Thus, the user may correct or otherwise provide for enhanced accuracy inatomized dataset generation “in-situ,” or during the dataset ingestionand/or graph formation processes. As another example, data from a numberof sources may include dataset metadata 603 a (e.g., descriptive data orinformation specifying dataset attributes), dataset data 603 b (e.g.,some or all data stored in system repositories 640 a, which may storegraph data), schema data 603 c (e.g., sources, such as schema.org, thatmay provide various types and vocabularies), ontology data 603 d fromany suitable ontology (e.g., data compliant with Web Ontology Language(“OWL”), as maintained by the World Wide Web Consortium (“W3C”)), andany other suitable types of data sources.

In one example, data classifier 634 may be configured to analyze acolumn of data to infer a datatype of the data in the column. Forinstance, data classifier 634 may analyze the column data to infer thatthe columns include one of the following datatypes: an integer, astring, a Boolean data item, a categorical data item, a time, etc.,based on, for example, data from UI data 601 d (e.g., data from a UIrepresenting an annotation), as well as based on data from data 603 a to603 d. In another example, data classifier 634 may be configured toanalyze a column of data to infer a data classification of the data inthe column (e.g., where inferring the data classification may be moresophisticated than identifying or inferring a datatype). For example,consider that a column of ten (10) integer digits is associated with anunspecified or unidentified heading. Data classifier 634 may beconfigured to deduce the data classification by comparing the data todata from data 601 d, and from data 603 a to 603 d. Thus, the column ofunknown 10-digit data in data 601 a may be compared to 10-digit columnsin other datasets that are associated with an annotation of “phonenumber.” Thus, data classifier 634 may deduce the unknown 10-digit datain data 601 a includes phone number data.

In the above example, consider that data in the column (e.g., in a CSVor XLS file) may be stored in a system of layer files, whereby raw dataitems of a dataset is stored at layer zero (e.g., in a layer zero (“L0”)file). The datatype of the column (e.g., string datatype) may be storedat layer one (e.g., in a layer one (“L1”) file, which may be linked tothe data item at layer zero in the L0 file). An inferred datasetattribute, such as a “derive annotation,” may indicate a column of ten(10) integer digits can be classified as a “phone number,” which may bestored as annotative description data stored at layer two (e.g., in alayer two (“L2”) file, which may be linked to the classification of“integer” at layer one, which, in turn, may be linked to the 10 digitsin a column at layer zero). While not shown in FIG. 6, the system oflayer files may be adaptive to add or remove data items, under controlof the dataset ingestion controller 620 (or any of its constituentcomponents), at the various layers as datasets are expanded or modifiedto include additional data as well as annotations, references,statistics, etc. Another example of a layer system is described inreference to FIG. 12, among other figures herein.

In yet another example, inference engine 632 may receive data (e.g., adatatype or data classification, or both) from an attribute correlator663. As shown, attribute correlator 663 may be configured to receivedata, including attribute data (e.g., dataset attribute data), fromdataset ingestion controller 620. Also, attribute correlator 663 may beconfigured to receive data from data sources (e.g., UI-related/userinputted data 601 d, and data 603 a to 603 d), and from systemrepositories 640 a. Further, attribute correlator 663 may be configuredto receive data from one or more of external public repository 640 b,external private repository 640 c, dominion dataset attribute data store662, and dominion user account attribute data store 662, or from anyother source of data. In the example shown, dominion dataset attributedata store 662 may be configured to store dataset attribute data forwhich collaborative dataset consolidation system may have dominion,whereas dominion user account attribute data store 662 may be configuredto store user or user account attribute data for data in its domain.

Attribute correlator 663 may be configured to analyze the data to detectpatterns that may resolve an issue. For example, attribute correlator663 may be configured to analyze the data, including datasets, to“learn” whether unknown 10-digit data is likely a “phone number” ratherthan another data classification. In this case, a probability may bedetermined that a phone number is a more reasonable conclusion based on,for example, regression analysis or similar analyses. Further, attributecorrelator 663 may be configured to detect patterns or classificationsamong datasets and other data through the use of Bayesian networks,clustering analysis, as well as other known machine learning techniquesor deep-learning techniques (e.g., including any known artificialintelligence techniques). Attribute correlator 663 also may beconfigured to generate enrichment data 607 b that may includeprobabilistic or predictive data specifying, for example, a dataclassification or a link to other datasets to enrich a dataset.According to some examples, attribute correlator 663 may further beconfigured to analyze data in dataset 601 a, and based on that analysis,attribute correlator 663 may be configured to recommend or implement oneor more added columns of data. To illustrate, consider that attributecorrelator 663 may be configured to derive a specific correlation basedon data 607 a that describe three (3) columns, whereby those threecolumns are sufficient to add a fourth (4th) column as a derived column.In some cases, the data in the 4th column may be derived mathematicallyvia one or more formulae. One example of a derived column is describedin FIG. 13 and elsewhere herein. Therefore, additional data may be usedto form, for example, additional “triples” to enrich or augment theinitial dataset.

In yet another example, inference engine 632 may receive data (e.g.,enrichment data 607 b) from a dataset attribute manager 661, whereenrichment data 607 b may include derived data or link-related data toform collaborative datasets. Consider that attribute correlator 663 candetect patterns in datasets in repositories 640 a to 640 c, among othersources of data, whereby the patterns identify or correlate to a subsetof relevant datasets that may be linked with the dataset in data 601 a.The linked datasets may form a collaborative dataset that is enrichedwith supplemental information from other datasets. In this case,attribute correlator 663 may pass the subset of relevant datasets asenrichment data 607 b to dataset enrichment manager 636, which, in turn,may be configured to establish the links for a dataset in 601 b. Asubset of relevant datasets may be identified as a supplemental subsetof supplemental enrichment data 607 b. Thus, converted dataset 601 c(i.e., an atomized dataset) may include links to establish collaborativedatasets formed with collaborative datasets.

Dataset attribute manager 661 may be configured to receive correlatedattributes derived from attribute correlator 663. In some cases,correlated attributes may relate to correlated dataset attributes basedon data in data store 662 or based on data in data store 664, amongothers. Dataset attribute manager 661 also monitors changes in datasetand user account attributes in respective repositories 662 and 664. Whena particular change or update occurs, collaboration manager 660 may beconfigured to transmit collaborative data 605 to user interfaces ofsubsets of users that may be associated the attribute change (e.g.,users sharing a dataset may receive notification data that the datasethas been created, modified, linked, updated, associated with a comment,associated with a request, queried, or has been associated with anyother dataset interactions).

Therefore, dataset enrichment manager 636, according to some examples,may be configured to identify correlated datasets based on correlatedattributes as determined, for example, by attribute correlator 663. Thecorrelated attributes, as generated by attribute correlator 663, mayfacilitate the use of derived data or link-related data, as attributes,to form associate, combine, join, or merge datasets to formcollaborative datasets. A dataset 601 b may be generated by enriching adataset 601 a using dataset attributes to link to other datasets. Forexample, dataset 601 a may be enriched with data extracted from (orlinked to) other datasets identified by (or sharing similar) datasetattributes, such as data representing a user account identifier, usercharacteristics, similarities to other datasets, one or more other useraccount identifiers that may be associated with a dataset, data-relatedactivities associated with a dataset (e.g., identity of a user accountidentifier associated with creating, modifying, querying, etc. aparticular dataset), as well as other attributes, such as a “usage” ortype of usage associated with a dataset. For instance, a virus-relateddataset (e.g., Zika dataset) may have an attribute describing a contextor usage of dataset, such as a usage to characterize susceptiblevictims, usage to identify a vaccine, usage to determine an evolutionaryhistory of a virus, etc. So, attribute correlator 663 may be configuredto correlate datasets via attributes to enrich a particular dataset.

According to some embodiments, one or more users or administrators of acollaborative dataset consolidation system may facilitate curation ofdatasets, as well as assisting in classifying and tagging data withrelevant datasets attributes to increase the value of the interconnecteddominion of collaborative datasets. According to various embodiments,attribute correlator 663 or any other computing device operating toperform statistical analysis or machine learning may be configured tofacilitate curation of datasets, as well as assisting in classifying andtagging data with relevant datasets attributes. In some cases, datasetingestion controller 620 may be configured to implement third-partyconnectors to, for example, provide connections through whichthird-party analytic software and platforms (e.g., R, SAS, Mathematica,etc.) may operate upon an atomized dataset in the dominion ofcollaborative datasets. For instance, dataset ingestion controller 620may be configured to implement API endpoints to provide or accessfunctionalities provided by analytic software and platforms, such as R,SAS, Mathematica, etc.

FIG. 7 is a diagram depicting operation of an example of an inferenceengine, according to some embodiments. Diagram 700 depicts an inferenceengine 780 including a data classifier 781 and a dataset enrichmentmanager 783, whereby inference engine 780 is shown to operate on data706 (e.g., one or more types of data described in FIG. 6), and furtheroperates on annotated tabular data representations of dataset 702,dataset 722, dataset 742, and dataset 762. Dataset 702 includes rows 710to 716 that relate each population number 704 to a city 702. Dataset 722includes rows 730 to 736 that relate each city 721 to both ageo-location described with a latitude coordinate (“lat”) 724 and alongitude coordinate (“long”) 726. Dataset 742 includes rows 750 to 756that relate each name 741 to a number 744, whereby column 744 omits anannotative description of the values within column 744. Dataset 762includes rows, such as row 770, that relate a pair of geo-coordinates(e.g., latitude coordinate (“lat”) 761 and a longitude coordinate(“long”) 764) to a time 766 at which a magnitude 768 occurred during anearthquake.

Inference engine 780 may be configured to detect a pattern in the dataof column 704 in dataset 702. For example, column 704 may be determinedto relate to cities in Illinois based on the cities shown (or based onadditional cities in column 704 that are not shown, such as Skokie,Cicero, etc.). Based on a determination by inference engine 780 thatcities 704 likely are within Illinois, then row 716 may be annotated toinclude annotative portion (“IL”) 790 (e.g., as derived supplementaldata) so that Springfield in row 716 can be uniquely identified as“Springfield, Ill.” rather than, for example, “Springfield, Nebr.” or“Springfield, Mass.” Further, inference engine 780 may correlate columns704 and 721 of datasets 702 and 722, respectively. As such, eachpopulation number in rows 710 to 716 may be correlated to correspondinglatitude 724 and longitude 726 coordinates in rows 730 to 734 of dataset722. Thus, dataset 702 may be enriched by including latitude 724 andlongitude 726 coordinates as a supplemental subset of data. In the eventthat dataset 762 (and latitude 724 and longitude 726 data) are formatteddifferently than dataset 702, then latitude 724 and longitude 726 datamay be converted to an atomized data format (e.g., compatible with RDF).Thereafter, a supplemental atomized dataset can be formed by linking orintegrating atomized latitude 724 and longitude 726 data with atomizedpopulation 704 data in an atomized version of dataset 702. Similarly,inference engine 780 may correlate columns 724 and 726 of dataset 722 tocolumns 761 and 764. As such, earthquake data in row 770 of dataset 762may be correlated to the city in row 734 (“Springfield, Ill.”) ofdataset 722 (or correlated to the city in row 716 of dataset 702 via thelinking between columns 704 and 721). The earthquake data may be derivedvia latitude and longitude coordinate-to-earthquake correlations assupplemental data for dataset 702. Thus, new links (or triples) may beformed to supplement population data 704 with earthquake magnitude data768.

Inference engine 780 also may be configured to detect a pattern in thedata of column 741 in dataset 742. For example, inference engine 780 mayidentify data in rows 750 to 756 as “names” without an indication of thedata classification for column 744. Inference engine 780 can analyzeother datasets to determine or learn patterns associated with data, forexample, in column 741. In this example, inference engine 780 maydetermine that names 741 relate to the names of “baseball players.”Therefore, inference engine 780 determines (e.g., predicts or deduces)that numbers in column 744 may describe “batting averages.” As such, acorrection request 796 may be transmitted to a user interface to requestcorrective information or to confirm that column 744 does includebatting averages. Correction data 798 may include an annotation (e.g.,batting averages) to insert as annotation 794, or may include anacknowledgment to confirm “batting averages” in correction request data796 is valid. Note that the functionality of inference engine 780 is notlimited to the examples describe in FIG. 7 and is more expansive than asdescribed in the number of examples. In some examples, determination ofa column header, such as column header 744, may be associated with anannotation that may be automatically determined (e.g., based on inferreddata that determines a annotative description of data for a column), ormay be entered semi-automatically or manually.

FIG. 8 is a diagram depicting a flow diagram as an example of ingestingan enhanced dataset into a collaborative dataset consolidation system,according to some embodiments. Diagram 800 depicts a flow for an exampleof inferring dataset attributes and generating an atomized dataset in acollaborative dataset consolidation system. At 802, data representing adataset having a data format may be received into a collaborativedataset consolidation system. The dataset may be associated with anidentifier or other dataset attributes with which to correlate thedataset. At 804, a subset of data of the dataset is interpreted againstsubsets of data (e.g., columns of data) for one or more dataclassifications (e.g., datatypes) to infer or derive at least aninferred attribute for a subset of data (e.g., a column of data). Insome examples, the subset of data may relate to a columnarrepresentation of data in a tabular data format, or CSV file, with, forexample, columns annotated. Annotations may include descriptions of adata type (e.g., string, numeric, categorical, etc.), a dataclassification (e.g., a location, such as a zip code, etc.), or anyother data or metadata that may be used to locate in a search or to linkwith other datasets.

To illustrate, consider that a subset of data attributes (e.g., datasetattributes) may be identified with a request to create a dataset (e.g.,to create a linked dataset), or to perform any other operation (e.g.,analysis, data insight generation, dataset atomization, etc.). Thesubset of dataset attributes may include a description of the datasetand/or one or more annotations the subset of dataset attributes.Further, the subset of dataset attributes may include or refer to datatypes or classifications that may be association with, for example, acolumn in a tabular data format (e.g., prior to atomization or as analternate view). Note that in some examples, one or more data attributesmay be stored in one or more layer files that include references orpointers to one or more columns in a table for a set of data. Inresponse to a request for a search or creation of a dataset, thecollaborative dataset consolidation system may retrieve a subset ofatomized datasets that include data equivalent to (or associated with)one or more of the dataset attributes.

So if a subset of dataset attributes includes alphanumeric characters(e.g., two-letter codes, such as “AF” for Afghanistan), then a columncan be identified as including country code data (e.g., a columnincludes data cells with AF, BR, CA, CN, DE, JP, MX, UK, US, etc.).Based on the country codes as a “data classification,” the collaborativedataset consolidation system may correlate country code data in otheratomized datasets to a dataset of interest (e.g., a newly-createddataset, an analyzed dataset, a modified dataset (e.g., with addedlinked data), a queried dataset, etc.). Then, the system may retrieveadditional atomized datasets that include country codes to form acollaborative dataset. The consolidation may be performed automatically,semi-automatically (e.g., with at least one user input), or manually.Thus, these datasets may be linked together by country codes. Note thatin some cases, the system may implement logic to “infer” that twoletters in a “column of data” of a tabular, pre-atomized datasetincludes country codes. As such, the system may “derive” an annotation(e.g., a data type or classification) as a “country code.” Therefore,the derived classification of “country code” may be referred to as aderived attribute, which, for example, may be stored in a layer two (2)data file, examples of which are described herein (e.g., FIGS. 6 and 12,among others). A dataset ingestion controller may be configured toanalyze data and/or dataset attributes to correlate the same overmultiple datasets, the dataset ingestion controller being furtherconfigured to infer a data type or classification of a grouping of data(e.g., data disposed in a column or any other data arrangement),according to some embodiments.

At 806, the subset of the data may be associated with annotative dataidentifying the inferred attribute. Examples of an inferred attributeinclude the inferred “baseball player” names annotation and the inferred“batting averages” annotation, as described in FIG. 7. At 808, thedataset may be converted from the data format to an atomized datasethaving a specific format, such as an RDF-related data format. Theatomized dataset may include a set of atomized data points, whereby eachdata point may be represented as an RDF triple. According to someembodiments, inferred dataset attributes may be used to identify subsetsof data in other dataset, which may be used to extend or enrich adataset. An enriched dataset may be stored as data representing “anenriched graph” in, for example, a triplestore or an RDF store (e.g.,based on a graph-based RDF model). In other cases, enriched graphsformed in accordance with the above, and any implementation herein, maybe stored in any type of data store or with any database managementsystem.

FIG. 9 is a diagram depicting a dataset creation interface, according tosome embodiments. Diagram 900 depicts a dataset creation interface 902as an example of a computerized tool to form collaborative datasets.Diagram 900 also depicts a collaborative dataset consolidation system910, which is shown to include a repository 940, a user interface (“UI”)element generator 980, a programmatic interface 990, and a processor999. User interface (“UI”) element generator 980 may be configured togenerate data to form user interface elements, and may be furtherconfigured to cause presentation of user interface elements on a userinterface to facilitate data signal detection to initiate a datasetcreation process, according to various examples. In one or moreimplementations, elements depicted in diagram 900 of FIG. 9 may includestructures and/or functions as similarly-named or similarly-numberedelements depicted in other drawings.

Dataset creation interface 902 may be used to create, or initiatecreation of, a collaborative dataset via a computing device (not shown).In the example shown, dataset creation interface 902 includes adescriptive title 901, a number of user interface elements to facilitatedataset creation, such as a search field 921, a dataset title 903, afile upload interface 906, a “create dataset” activation input 904, an“open” activation input 941, a “private” activation input 942, a“restricted” activation input 944, and any other type of user interfaceelement that may be used to create datasets that, in turn, may betransformed into atomized datasets, such as an atomized dataset storedin repository 940.

In this example, consider that dataset creation interface 902 isconfigured to create a dataset directed to earthquake-related data. Textentered into dataset title field 903 may be parsed, analyzed andassociated with the created dataset to identify the dataset and to makeat least some of the text in the title entered in field 903 searchable(e.g., in search field 921). Thus, the term “earthquake” may enable thecreated dataset to be returned in search results responsive to a searchof “earthquake data” in search field 921. “Open” activation input 941may be configured to activate, if selected, logic to classify thedataset as “publicly available data” such that anyone may access (e.g.,search, view, query, download, modify, etc.) the earthquake datasetcreated using dataset creation interface 902. “Private” activation input942 may be configured to activate, if selected, logic to classify thedataset as “private data” such that no other dataset may link to the“earthquake dataset” and no one may access the dataset, unlessauthorization is granted to do so. “Restricted” activation input 944 maybe configured to activate, if selected, logic to classify the dataset as“restricted data.” In some examples, metadata, such as search terms,annotations, etc., may be publicly exposed or searchable, whereas thedata of the “earthquake” dataset may be inaccessible. Consequently, adata practitioner that owns a particular dataset may allow others tofind the dataset, and if there is interest by another user, the datapractitioner may provide authorization the interested user to access thedataset with restrictions (e.g., usage limitations, time limitations,etc.). In some cases, authorization may be made in exchange forremuneration. “Restricted” activation input 944 may be also configuredto modify different levels of restricted access.

An owner of the “earthquake” dataset may offer various levels ofpermissions for a dataset or a particular user. For example, permissionsmay be selectably configured to enable or disable an ability to beidentified in a search, enable or disable viewing of the dataset, enableor disable an ability to query, enable or disable an ability to downloadthe dataset, enable or disable ability to modify the dataset, ability tomodify time intervals during which the dataset is accessible, etc.

According to some embodiments, user interface element generator 980 maybe configured to cause the generation of a user interface element fordataset creation interface 902 or any other interface, such as thosedescribed herein. A user interface element may be generated by userinterface element generator 980 as a graphical control element toprovide a visual component for presentation to a user. The visualcomponent may be configured to convey data stored in a computing deviceor functionality of a computing device to, for example, renderspecialized functionalities described herein. According to someexamples, user interface element generator 980 may be configured togenerate at least one user interface element and/or at least one subsetof executable instructions for implementation at either a clientcomputing device or a server computing device, or a combination thereof.Thus, user interface elements may be configured to facilitateclient-side computations or server-side computations, as well asdistributed computing among one or more client computing devices, one ormore server computing devices, and one or more other computing devices.Examples of user interface elements include, but are not limited to,subsets of executable instructions (e.g., software components, modules,etc., such as “widgets,” APIs, etc.) that facilitate implementations of(1) data signals for user input controls (e.g., initiation of actionsand processes via buttons, menus, text fields, hypertext links, etc.)within an interface, (2) data signals for navigation to access one ormore computing devices via links, tabs, scrollbars, etc., (3) datasignals for modifying (via computations) or manipulating data values(e.g., using labels, check boxes, radio buttons, sliders, etc.), (4)data signals for displaying and manipulating computational results ordata outputs, (5) data signals for implementing a data entry interfacefor accessing data, querying data, etc. (e.g., via a modal window,etc.), and (6) any other action or process configurable to create acollaborative dataset or otherwise implement a collaborative dataset(e.g., analyzing, sharing, and querying a dataset, among otherimplementations).

Further to the example shown, a processor 999 may be configured, inaccordance with executing program code, to facilitate selection of anicon 905 representing a set of data. The selection may be implementedvia a pointer 907 (and associated data signals), which enables a set ofdata 105 to enter an uploading process. Icon 905 and pointer 907 areexamples of user interface elements generated by user interface elementgenerator 980. Processor 999 may detect a data signal originating fromdataset creation interface 902, responsive to activation of “createdataset” user interface 904. Processor 999 may initiate dataset creationprocess or may perform one or more portions thereof (e.g., including theprocess of creating a dataset). According to some embodiments, processor999 and/or its functionalities may be disposed and/or performed ateither a client computing device or a remote computing device (e.g., aserver), or may be disposed or performed at multiple computing devices,including networked or non-networked computing devices. Similarly,collaborative dataset consolidation system 910 may be include logic thatmay be implemented either at one or more client computing devices or oneor more remote computing devices, or may be implemented at multiplecomputing devices. Either one of user interface element generator 980and a programmatic interface 990 or both, may be implemented at a clientcomputing device, at a remote computing device, or at multiple computingdevices. Further, the functionalities of user interface elementgenerator 980 and a programmatic interface 990 may be performed inseries, in parallel, or in any order. The above-described examples ofstructures and functionalities in diagram 900 are not intended to belimiting, and such structures and functionalities may be implementedwith additional breadth.

FIG. 10 is a diagram of an example of a user interface depictingprogression of phases during creation of a dataset, according to someembodiments. Diagram 1000 depicts an interface 1002, which depictsprogression of phases during creation of a dataset. For example,progression user interface element 1020 is configured to present thephases of creating a dataset including an uploading user interfaceelement 1022 to depict the process of uploading data, such as a raw datafile, initiated by activating a “create dataset” input 904. Progressionuser interface element 1020 also is shown to include an insight userinterface element 1024 to specify a status for an insight phase of thedataset creation process (e.g., identifying dataset attributes,including derived or inferred dataset attributes), a linking userinterface element 1026 to specify the status of a linking phase duringatomized datasets are linked (e.g., including links to protecteddatasets), and a “complete” user interface element 1028, which specifiesthe completion of the data creation process.

According to some examples, interface 1002 may also include userinterface elements that provide additional guidance or enhance theprogression of the data creation process. User interface element 1012may be configured as a user input to generate data signals to initiateassociation of summary data to the dataset, such as adding a data filerepresenting a logo, or other graphical imagery of interest, as well asany other summary information (e.g., hyperlinks to sites from which rawdata files were sourced, etc.), including text, as well as uploadingcode or programmatic instructions, among other things. User interfaceelement 1012 also can facilitate uploading non-dataset data, such as anydata or files that may provide context for a dataset to enrichunderstanding of the data. User interface element 1030 may be configuredto convey information as to the user or owner of the dataset, and may befurther configured to operate as a user input to generate data signals,which may initiate a transition to an interface that presents useraccount information (not shown). User interface element 1040 may beconfigured to convey information regarding one or more original filesthat are used in the dataset creation process. As shown, user interfaceelement 1040 conveys the type of data file (e.g., .XLS file), a datafile title (e.g., “Earthquake M4_5 and higher”), a file size (e.g.,168.5 KB), and an age (e.g., when the data file was last uploaded, suchas 10 days or seconds ago, etc.), and the like.

User interface element 1014 may be configured as a user input togenerate data signals to initiate inclusion of another set of data forcreating a new dataset. In turn, the new dataset may be linkedautomatically to the previously-created dataset. Automatically-generatedlinks may be formed among datasets, such as atomized datasets, based oninferred or derived dataset attributes, authorized access to protecteddatasets, etc. In some instances, a user interface may provide a userinput (not shown) to facilitate manual linking among datasets. Further,interface 1002 also may include user interface elements 1001, 1003,1005, 1007, and 1009, each of which may be configured as a user input togenerate data signals to initiate a particular function. For example,user interface element 1001 may be configured to generate data signalsto associate a short description to the dataset, and user interfaceelement 1003 may be configured to generate data signals to associate“tags” (e.g., key words or symbols) to the dataset so that tags may beused for identifying the subset during, for example, a keyword search.User interface element 1005 may be configured to generate data signalsto add files (e.g., .CSV, .XLS, .PDF, etc.), or portions thereof, tocreation of a collaborative dataset, whereby added files mayautomatically linked to a dataset. In some cases, user input 1005performs similar functions as user input 1014. User interface element1007 may be configured to generate data signals to associate a narrativeto the dataset, whereby the narrative may be of sufficient size toconvey sufficient detail to those potential collaborators that may beinterested in using the dataset for their own or different purposes. Inat least one example, user interface element 1007 may be configured togenerate an overlay window (over interface 1002), the overlay windowincluding an interface (not shown) to enter text or other symbols intothe interface. In some cases, the interface of the overlay window mayinclude a text-to-HTML conversion too, such as MARKDOWN™ developed byJohn Gruber. User interface element 1009 may be configured to generatedata signals to associate data representing a license, and, thus,optional legal requirements to the dataset. In some examples, selectionof user interface elements 1001, 1003, and 1009 may cause transition toanother interface, such as interface 1102 of FIG. 11.

FIG. 11 is a diagram of an example of a user interface configured toenhance dataset attribute data for a dataset, according to someembodiments. Diagram 1100 depicts an interface 1102, which depictsvarious user interface elements with which to add or modify datasetattributes, which, in turn, may be implemented as metadata. As shown, adataset title field 1103 a may be configured to accept text inputs toassociate “earthquake” and “data” to a dataset identified as dataset(“/Earthquake data”) 1101. User interface 1102 may also include thefollowing user interface elements: (1) a description field 1103 b toenter a description of the dataset, (2) a tag field 1105 to add anynumber of tags, and (3) a pull-down menu 1107 to select an applicablelicense type for accessing and using the data of dataset 1101. In someexamples, user interface elements 1001, 1003, and 1009 of FIG. 10 maycause a transition to a user interface 1102 of FIG. 11 to enter data indescription field 1103 b, tag field 1105, and pull-down menu 1107,respectively. According to some examples, activation of pull-down menu1107 may expose any of the following license types for selection, and,thus association to dataset 1101: Public Domain Dedication Statement,Open Data Commons Public Domain Dedication and License (“PDDL”), PublicDomain Dedication License (“CC0 1.0 Universal”), Attribution 2.0 GenericLicense (“CC BY 2.0”), Open Data Commons Attribution License (“ODC-BY”),Attribution-ShareAlike 3.0 Unported (“CC BY-SA 3.0”), Open Data CommonsOpen Database License (“ODbL”), Attribution-NonCommercial-ShareAlike(“CC BY-NC-SA”), among other license types.

FIG. 12 is a diagram depicting an example of a data ingestion controllerconfigured to generate a set of layer data files, according to someexamples. Diagram 1200 depicts a dataset ingestion controller 1220communicatively coupled to a dataset attribution manager 1261, and isfurther coupled communicatively to one or both of a user interface(“UP”) element generator 1280 and a programmatic interface 1290 toexchange data and/or commands (e.g., executable instructions) with auser interface, such as a collaborative dataset interface 1202.According to various examples, dataset ingestion controller 1220 and itsconstituent elements may be configured to detect exceptions or anomaliesamong subsets of data (e.g., columns of data) of an imported or uploadedset of data, and to facilitate corrective actions to negate dataanomalies, whether automatically, semi-automatically (e.g., one or morecalculated or predicted solutions from which a user may select), andmanually (e.g., the user may annotate or otherwise correct exceptions).Further, dataset ingestion controller 1220 may be configured toidentify, infer, and/or derive dataset attributes with which to: (1)associate with a dataset via, for example, annotations (e.g., columnheaders), (2) determine a datatype (e.g., as a dataset attribute) for asubset of data in the dataset, (3) determine an inferred datatype forthe subset of data (e.g., as an inferred dataset attribute), (4)determine a data classification for a subset of data in the dataset,(5), determine an inferred data classification, (6) derive one or moredata structures, such as the creation of an additional column of data(e.g., temperature data expressed in degrees Fahrenheit) based on acolumn of temperature data expressed in degrees Celsius, (7) identifysimilar or equivalent dataset attributes associated withpreviously-uploaded or previously-accessed datasets to “enrich” thedataset by linking the dataset via the dataset attributes to otherdatasets, and (8) perform other data actions.

Dataset attribution manager 1261 and its constituent elements may beconfigured to manage dataset attributes over any number of datasets,including correlating data in a dataset against any number of datasetsto, for example, determine a pattern that may be predictive of a datasetattribute. For example, dataset attribution manager 1261 may analyze acolumn that includes a number of cells that each includes five digitsand matches a pattern of valid zip codes. Thus, dataset attributionmanager 1261 may classify the column as containing zip code data, whichmay be used to annotate, for example, a column header as well as forminglinks to other datasets with zip code data. One or more elementsdepicted in diagram 1200 of FIG. 12 may include structures and/orfunctions as similarly-named or similarly-numbered elements depicted inother drawings, or as otherwise described herein, in accordance with oneor more examples. Note, too, that while data structures described inthis example, as well as in other examples described herein, may referto a tabular data format, various implementation herein may be describedin the context of any type of data arrangement. The descriptions ofusing a tabular data structure are illustrative and are not intended tobe limiting. Therefore, the various implementations described herein maybe applied to many other data structures.

Dataset ingestion controller 1220, at least in some embodiments, may beconfigured to generate layer file data 1250, which may include a numberof data arrangements that each may constitute a layer file. Notably, alayer file may be used to enhance, modify or annotate data associatedwith a dataset, and may be implemented as a function of contextual data,which includes data specifying one or more characteristics of thecontext or usage of the data. Data and datasets may be enhanced,modified or annotated based on contextual data, such as data-relatedcharacteristics (e.g., type of data, qualities and quantities of dataaccesses, including queries, purpose or objective of datasets, such asderiving vaccines for Zika virus, etc.), time of day, user-relatedcharacteristics (e.g., type of user, demographics of user, citizenshipof user, location of user, etc.), and other contextually-relatedcharacteristics that may guide creation of a dataset or the linkingthereof. Note, too, that the use of layer files need not modify theunderlying data. Further to the example shown, a layer file may includea link or pointer that references a location (directly or indirectly) atwhich related dataset data persists or may be accessed. Arrowheads areused in this example to depict references to layered data. A layer filemay include layer property information describing how to treat (i.e.,use) the data in the dataset (e.g., functionally, visually, etc.). Insome instances, “layer files” may be layered upon (e.g., in referenceto) another layer, whereby layers may be added, for example, tosequentially augment underlying data of the dataset. Therefore, layerfiles may provide enhanced information regarding an atomized dataset,and adaptability to present data or consume data based on the context(e.g., based on a user or data practitioner viewing or querying thedata, a time of day, a location of the user, the dataset attributesassociated with linked datasets, etc.). A system of layer files may beadaptive to add or remove data items, under control of the datasetingestion controller 1220 (or any of its constituent components), at thevarious layers responsive to expansions and modifications of datasets(e.g., responsive to additional data, such as annotations, references,statistics, etc.).

To illustrate generation of layer file data 1250, consider the followingexample. Dataset ingestion controller 1220 is configured to receive datafrom data file 1201 a, which may be arranged in a tabular formatincluding columns and rows (e.g., based on .XLS file format). In thisexample, the tabular data is depicted at layer (“0”) 1251. In thisexample, layer (“0”) 1251 includes a data structure including subsets ofdata 1255, 1256, and 1257. As shown, subset of data 1255 is shown to bea column of numeric data associated with “Foo” as column header 1255 a.Subset of data 1256 is shown to be a column of categorical data (e.g.,text strings representing colors) associated with “Bar” as column header1256 a. And subset of data 1257 is a column of string data that may beof numeric datatype and is without an annotated column header (“???”)1257 a.

Next, consider operation of dataset ingestion controller 1220 inrelation to ingested data (“layer ‘0’”) 1251. Dataset ingestioncontroller 1220 includes a dataset analyzer 1230, which may beconfigured to analyze data 1251 to detect data entry exceptions andirregularities (e.g., whether a cell is empty or includes non-usefuldata, whether a cell includes non-conforming data, whether there are anymissing annotations or column headers, etc.). In this example, datasetanalyzer 1230 may analyze data in columns of data 1255, 1256, and 1257to detect that column 1257 is without descriptive data representing acolumn header 1257 a. As shown, dataset analyzer 1230 includes aninference engine 1232 that may be configured to infer or interpret adataset attribute (e.g., as a derived attribute) based on analyzed data.Further, inference engine 1232 may be configured to infer correctiveactions to resolve or compensate for the exceptions and irregularities,and to identify tentative data enrichments (e.g., by joining with, orlinking to, other datasets) to extend the data beyond that which is indata file 1201 a. So in this example, dataset analyzer 1230 may instructinference engine 1232 to participate in correcting the absence of thecolumn description.

Inference engine 1232 is shown to include a data classifier 1234, whichmay be configured to classify subsets of data (e.g., each subset of dataas a column) in data file 1201 a as a particular data classification,such as a particular data type, a particular annotation, etc. Accordingto some examples, data classifier 1234 may be configured to analyze acolumn of data to infer a datatype of the data in the column. Forinstance, data classifier 1234 may analyze the column data toautomatically infer that the columns include one of the followingdatatypes: an integer, a string, a Boolean data item, a categorical dataitem, a time, etc. In the example shown, data classifier 1234 maydetermine or infer, automatically or otherwise, that data in columns1255 and 1256 are a numeric datatype and categorical data type,respectively, and such information may be stored as dataset attribute(“numeric”) 1252 a and dataset attribute (“categorical”) 1252 b at layer(“1”) 1252 (e.g., in a layer file). Similarly, data classifier 1234 maydetermine or infer data in column 1257 is a numeric datatype and may bestored as dataset attribute (“numeric”) 1252 c at layer 1252. Thedataset attributes in layer 1252 are shown to reference respectivecolumns via, for example, pointers.

Data classifier 1234 may be configured to analyze a column of data toinfer or derive a data classification for the data in the column. Insome examples, a datatype, a data classification, etc., as well anydataset attribute, may be derived based on known data or information(e.g., annotations), or based on predictive inferences using patterns indata 1203 a to 1203 d. As an example of the former, consider that dataclassifier 1234 may determine data in columns 1255 and 1256 can beclassified as a “date” (e.g., MM/DD/YYYY) and a “color,” respectively.“Foo” 1255 a, as an annotation, may represent the word “date,” which canreplace “Foo” (not shown). Similarly, “Bar” 1256 a may be an annotationthat represents the word “color,” which can replace “Bar” (not shown).Using text-based annotations, data classifier 1234 may be configured toclassify the data in columns 1255 and 1256 as “date information” and“color information,” respectively. Data classifier 1234 may generatedata representing as dataset attributes (“date”) 1253 a and (“color”)1252 b for storage as at layer (“2”) 1253 of a layer file, or in anyother layer file that references dataset attributes 1252 a and 1252 b atlayer 1252. As to the latter, a datatype, a data classification, etc.,as well any dataset attribute, may be derived based on predictiveinferences (e.g., via machine learning, etc.) using patterns in data1203 a to 1203 d. In this case, inference engine 1232 and/or dataclassifier 1234 may detect an absence of annotations for column header1257 a, and may infer that the numeric values in column 1257 eachincludes five digits, and match patterns of number indicative of validzip codes. Thus, dataset classifier 1234 may be configured to classify(e.g., automatically) the digits as constituting a “zip code,” and togenerate, for example, an annotation “postal code” to store as datasetattribute 1253 c. While not shown in FIG. 12, consider anotherillustrative example. Data classifier 1234 may be configured to “infer”that two letters in a “column of data” (not shown) of a tabular,pre-atomized dataset includes country codes. As such, data classifier1234 may “derive” an annotation (e.g., representing a data type, dataclassification, etc.) as a “country code,” such country codes AF, BR,CA, CN, DE, JP, MX, UK, US, etc. Therefore, the derived classificationof “country code” may be referred to as a derived attribute, which, forexample, may be stored in one or more layer files in layer file data1250.

Also, a dataset attribute, datatype, a data classification, etc. may bederived based on, for example, data from user interface data 1292 (e.g.,based on data representing an annotation entered via user interface1202). As shown, collaborative dataset interface 1202 is configured topresent a data preview 1204 of the set of data 1201 a (or datasetthereof), with “???” indicating that a description or annotation is notincluded. A user may move a cursor, a pointing device, such as pointer1279, or any other instrument (e.g., including a finger on atouch-sensitive display) to hover or select the column header cell. Anoverlay interface 1210 may be presented over collaborative datasetinterface 1202, with a proposed derived dataset attribute “Zip Code.” Ifthe inference or prediction is adequate, then an annotation directed to“zip code” may be generated (e.g., semi-automatically) upon acceptingthe derived dataset attribute at input 1271. Or, should the proposedderived dataset attribute be undesired, then a replacement annotationmay be entered into annotate field 1275 (e.g., manually), along withentry of a datatype in type field 1277. To implement, the replacementannotation will be applied as dataset attribute 1253 c upon activationof user input 1273. Thus, the “postal code” may be an inferred datasetattribute (e.g., a “derived annotation”) and may indicate a column of 5integer digits that can be classified as a “zip code,” which may bestored as annotative description data stored at layer two 1253 (e.g., ina layer two (“L2”) file). Thus, the “postal code,” as a “derivedannotation,” may be linked to the classification of “numeric” at layerone 1252. In turn, layer one 1252 data may be linked to 5 digits in acolumn at layer zero 1251). Therefore, an annotation, such as a columnheader (or any metadata associated with a subset of data in a dataset),may be derived based on inferred or derived dataset attributes, asdescribed herein.

Further to the example in diagram 1200, additional layers (“n”) 1254 maybe added to supplement the use of the dataset based on “context.” Forexample, dataset attributes 1254 a and 1254 b may indicate a date to beexpressed in U.S. format (e.g., MMDDYYYY) or U.K. format (e.g.,DDMMYYYY). Expressing the date in either the US or UK format may bebased on context, such as detecting a computing mobile device is ineither the United States or the United Kingdom. In some examples, dataenrichment manager 1236 may include logic to determine the applicabilityof a specific one of dataset attributes 1254 a and 1254 b based on thecontext. In another example, dataset attributes 1254 c and 1254 d mayindicate a text label for the postal code ought to be expressed ineither English or in Japanese. Expressing the text in either English orJapanese may be based on context, such as detecting a computing mobiledevice is in either the United States or Japan. Note that a “context”with which to invoke different data usages or presentations may be basedon any number of dataset attributes and their values, among otherthings.

In yet another example, data classifier 1234 may classify a column ofnumbers as either a latitudinal or longitudinal coordinate and may beformed as a derived dataset attribute for a particular column, which, inturn, may provide for an annotation describing geographic locationinformation (e.g., as a dataset attribute). For instance, considerdataset attributes 1252 d and 1252 e describe numeric datatypes forcolumns 1255 and 1257, respectively, and dataset attributes 1253 d and1253 e are classified as latitudinal coordinates in column 1255 andlongitudinal coordinates in column 1257. Dataset attribute 1254 e, whichidentifies a “country” that references dataset attributes 1253 d and1253, is shown associated with a dataset attribute 1254 f, which is anannotation as a name of the country and references dataset attribute1254 e. Similarly, dataset attribute 1254 g, which identifies a“distance to a nearest city” (e.g., a city having a threshold least acertain population level), may reference dataset attributes 1253 d and1253 e. Further, a dataset attribute 1254 h, which is an annotation as aname of the city for dataset attribute 1254 g, is also shown stored in alayer file at layer 1254.

Dataset attribution manager 1261 may include an attribute correlator1263 and a data derivation calculator 1265. Attribute correlator 1263may be configured to receive data, including attribute data (e.g.,dataset attribute data), from dataset ingestion controller 1220, as wellas data from data sources (e.g., UI-related/user inputted data 1292, anddata 1203 a to 1203 d), and from system repositories (not shown).Attribute correlator 1263 may be configured to analyze the data todetect patterns or data classifications that may resolve an issue, by“learning” or probabilistically predicting a dataset attribute throughthe use of Bayesian networks, clustering analysis, as well as otherknown machine learning techniques or deep-learning techniques (e.g.,including any known artificial intelligence techniques). Attributecorrelator 1263 may further be configured to analyze data in dataset1201 a, and based on that analysis, attribute correlator 1263 may beconfigured to recommend or implement one or more added or modifiedcolumns of data. To illustrate, consider that attribute correlator 1263may be configured to derive a specific correlation based on data 1207 athat describe two (2) columns 1255 and 1257, whereby those two columnsare sufficient to add a new column as a derived column.

In some cases, data derivation calculator 1265 may be configured toderive the data in a new column mathematically via one or more formulae,or by performing any computational calculation. First, consider thatdataset attribute manager 1261, or any of its constituent elements, maybe configured to generate a new derived column including the “name” 1254f of the “country” 1254 e associated with a geolocation indicated bylatitudinal and longitudinal coordinates in columns 1255 and 1257. Thisnew column may be added to layer 1251 data, or it can optionally replacecolumns 1255 and 1257. Second, consider that dataset attribute manager1261, or any of its constituent elements, may be configured to generatea new derived column including the “distance to city” 1254 g (e.g., adistance between the geolocation and the city). In some examples, dataderivation calculator 1265 may be configured to compute a lineardistance between a geolocation of, for example, an earthquake and anearest city of a population over 100,000 denizens. Data derivationcalculator 1265 may also be configured to convert or modify units (e.g.,from kilometers to miles) to form modified units based on the context,such as the user of the data practitioner. The new column may be addedto layer 1251 data. One example of a derived column is described in FIG.13 and elsewhere herein. Therefore, additional data may be used to form,for example, additional “triples” to enrich or augment the initialdataset.

Inference engine 1232 is shown to also include a dataset enrichmentmanager 1236. Data enrichment manager 1236 may be configured to analyzedata file 1201 a relative to dataset-related data to determinecorrelations among dataset attributes of data file 1201 a and otherdatasets 1203 b (and attributes, such as dataset metadata 1203 a), aswell as schema data 1203 c, ontology data 1203 d, and other sources ofdata. In some examples, data enrichment manager 1236 may be configuredto identify correlated datasets based on correlated attributes asdetermined, for example, by attribute correlator 1263 via enrichmentdata 1207 b that may include probabilistic or predictive dataspecifying, for example, a data classification or a link to otherdatasets to enrich a dataset. The correlated attributes, as generated byattribute correlator 1263, may facilitate the use of derived data orlink-related data, as attributes, to form associate, combine, join, ormerge datasets to form collaborative datasets. To illustrate, considerthat a subset of separately-uploaded datasets are included in datasetdata 1203 b, whereby each of these datasets in the subset include atleast one similar or common dataset attribute that may be correlatableamong datasets. For instance, each of datasets in the subset may includea column of data specifying “zip code” data. Thus, each of datasets maybe “linked” together via the zip code data. A subsequently-uploaded setof data into dataset ingestion controller 1220 that is determined toinclude zip code data may be linked via this dataset attribute to thesubset of datasets 1203 b. Therefore, a dataset formatted based on datafile 1201 a (e.g., as an annotated tabular data file, or as a CSV file)may be “enriched,” for example, by associating links between the datasetof data file 1201 a and other datasets 1203 b to form a collaborativedataset having, for example, and atomized data format.

FIG. 13 is a diagram depicting a user interface in association withgeneration and presentation of the derived subset of data, according tosome examples. Diagram 1300 depicts a user interface 1302 as an exampleof a computerized tool to modify collaborative datasets and to presentsuch modified datasets automatically, semi-automatically, or manually.User interface 1302 presents the data preview of a dataset that includesearthquake data and is entitled “Earthquake Data over 30 Day Period”1310. Data preview mode 1313 indicates that rows 1-10 of set of data1304, which includes 355 rows and 22 columns of data, are available topreview via a user interface element 1314 (e.g., via “scroll bar”). Thedataset originates from a set of data 1304, which is entitled“Earthquakes M4_5 and higher” and includes data describing geolocations,among other things (e.g., earthquake magnitudes, etc.), related toearthquakes having a magnitude 4.5 or higher.

Diagram 1300 depicts a dataset ingestion controller 1320, a datasetattribute manager 1360, a user interface generator 1380, and aprogrammatic interface 1390 configured to generate a derived column 1392and to present user interface elements 1312 to determine data signals tocontrol modification of the dataset. One or more elements depicted indiagram 1300 of FIG. 13 may include structures and/or functions assimilarly-named or similarly-numbered elements depicted in otherdrawings, or as otherwise described herein, in accordance with one ormore examples. As shown, the dataset may be presented in a tabularformat arranged in rows of data in accordance with a specific time(e.g., column 1303 data). The dataset is shown to include column data1306 a (i.e., latitude coordinates), column data 1306 b (i.e., longitudecoordinates), a column including depth data (e.g., depth of earthquakein kilometers from surface), a column 1308 including magnitude data(e.g., size of earthquake), a column including a type of magnitude ofthe earthquake (e.g., magnitude type “mb” refers to an earthquakemagnitude based on a short period body wave to compute the amplitude ofa P body-wave).

Logic in one or more of dataset ingestion controller 1320, datasetattribute manager 1360, user interface generator 1380, and programmaticinterface 1390 may be configured to analyze columns of data, such aslatitude column data 1306 a and longitude column data 1306 b, todetermine whether to derive one or more dataset attributes that mayrepresent a derived column of data. In the example shown, the logic isconfigured to generate a derived column 1392, which may be presentedautomatically in portion 1307 of user interface 1302 as anadditionally-derived column. As shown, derived column 1392 may includean annotated column heading “place,” which may be determinedautomatically or otherwise. Hence, the “place” of an earthquake can becalculated (e.g., using a data derivation calculator or other logic) todetermine a geographic location based on latitude and longitude data ofan earthquake event (e.g., column data 1306 a and 1306 b) at a distance1319 from a location of a nearest city. For example, an earthquake eventand its data in row 1305 may include derived distance data of “16 km,”as a distance 1319, from a nearest city “Kaikoura, New Zealand” inderived row portion 1305 a. According to some examples, a dataderivation calculator or other logic may perform computations to convert16 km into units of miles and store that data in a layer file. Data inderived column 1392 may be stored in a layer file that references theunderlying data of the dataset.

Further to user interface elements 1312, a number of user inputs may beactivated to guide the generation of a modify dataset. For example,input 1371 may be activated to add derived column 1392 to the dataset.Input 1373 may be activated to substitute and replace columns 1306 a and1306 b with derived column 1392. Input 1375 may be activated to rejectthe implementation of derived column 1392. In some examples, input 1377may be activated to manually convert units of distance from kilometersto miles. The generation of the derived column 1392 is but one example,and various numbers and types of derived columns (and data thereof) maybe determined.

FIGS. 14 and 15 are diagrams depicting examples of generating derivedcolumns and derived data, according to some examples. Diagram 1400 ofFIG. 14 and diagram 1500 of FIG. 15 depict a dataset ingestioncontroller 1420, a dataset attribute manager 1460, a user interfacegenerator 1480, and a programmatic interface 1490, one or more of whichincludes logic configured to each generate one or more derived columns.One or more elements depicted in diagrams 1400 and 1500 may includestructures and/or functions as similarly-named or similarly-numberedelements depicted in other drawings, or as otherwise described herein,in accordance with one or more examples.

In diagram 1400, the logic may be configured to generate derived column1422 (e.g., automatically) based on aggregating data in column 1404,which includes data representing a month, data in column 1406, whichincludes data representing a day, and data in column 1408, whichincludes data representing a year. Column 1422 may be viewed as acollapsed version of columns 1404, 1406, and 1408, according to someexamples. Therefore, the logic can generate derived column 1422 that canbe presented in user interface 1402 in a particular date format. Note,too, that column annotations, such as “month,” “day,” “year,” and“quantity,” can be used for linking and searching datasets as describedherein. Further, diagram 1400 depicts that a user interface 1402 mayoptionally include user interface elements 1471, 1473, and 1475 todetermine data signals to control modification of the dataset forrespectively “adding,” “substituting,” or “rejecting,” mentation ofderived column data.

In diagram 1500, the logic may be configured to generate derived columns1504, 1506, and 1508 based on data in column 1522 and related datacharacteristics. Derived columns 1504, 1506, and 1508 may also bepresented in user interface 1502. Derived columns 1504, 1506, and 1508may be viewed as expanded versions of column 1522, according to someexamples. Therefore, the logic can extract data with which to, forexample, infer additional or separate datatypes or data classifications.For example, the logic may be configured to split or otherwise transform(e.g., automatically) data in column 1522, which represents a “totalamount,” into derived column 1504, which represents a quantity, derivedcolumn 1506, which represents an amount, and derived column 1508, whichincludes data representing a unit type (e.g., milliliter, or “ml”).Note, too, that column annotations, such as “total amount,” “quantity,”“amount,” and “units,” can be used for linking and searching datasets asdescribed herein. Further, diagram 1500 depicts that a user interface1502 may optionally include user interface elements 1571, 1573, and 1575to determine data signals to control modification of the dataset forrespectively “adding,” “substituting,” or “rejecting,” implementation ofderived column data.

FIG. 16 is a diagram depicting a flow diagram as an example of enhancedcollaborative dataset creation based on a derived dataset attribute,according to some embodiments. Flow 1600 may be an example of initiatingcreation of the dataset, such as a collaborative dataset, based on aderived dataset attribute that is derived from a set of data. In someexamples, flow 1600 may be implemented in association with a userinterface. At 1602, data to form an input (as a user interface element)may be received via a user interface. For example, a processor executinginstruction data at a client computing device (or any other type ofcomputing device, including a server computing device) may receive datato form a user interface element, which may constitute a user input thatmay be presented in a user interface as, for example, a “create dataset”user input. In one or more cases, activation of a “create dataset” userinput can initiate creation of an atomized dataset based on the set ofdata, which, for example, may be raw data in data file (e.g., a tabulardata file, such as a XLS file, etc.). According to some examples, a datapreview of subsets of data may be presented in the user interface, thedata preview showing portions of a dataset or set of data. A datapreview may be generated (e.g., by a user interface element generator)to depict each subset of data as a column of data. In one example, adata view of a column of data may be presented with an unknown datasetattribute, whereby data may be received to annotate a column header toform an annotation to resolve the unknown dataset attribute. Theannotation may refer to a datatype, a data classification, or the like.An example of an unknown dataset attribute is depicted as unknown columnheader (“???”) 1257 a of FIG. 12.

Referring back to FIG. 16, a programmatic interface may be activated at1604 to facilitate the derivation of the dataset attribute that may beused in the creation of a dataset responsive to receiving the firstinput. The programmatic interface may be implemented as either hardwareor software, or a combination thereof. In some examples, theprogrammatic interface may be distributed as subsets of executable code(e.g., as scripts, etc.) to implement APIs in any number of computingdevices. In some embodiments, programmatic interface may be optional andmay be omitted.

At 1606, the set of data may be transformed from a first format to anatomized format to form an atomized dataset. In some examples, a requestto initiate creation of the dataset may cause transformation of adataset by, for example, deriving a dataset attribute based a subset ofdata. The derived dataset attribute may be used to form an annotation(e.g., derived annotation), or to form a derived column of data in whichdata is derived from one or more other subsets of data in a dataset, orfrom any other source of data. As such, an atomized dataset may begenerated to include data points associated with derived data. Theatomized dataset may be stored in a graph data structure, according tosome examples. In various examples, the transformation into an atomizeddataset, which includes one or more derived dataset attributes, may beperformed at a client computing device, a server computing device, or acombination of multiple computing devices.

At 1608, data representing an annotation may be presented at the userinterface, the annotation being based on the derived dataset attributefor the subset of data. Therefore, one or more various examples oflogic, as described herein, may be implemented to form derived subsetsof data, such as derived columns, which may be used to visually conveyan enhanced dataset that can be analyzed in relation to an objective ortheory. Further, a user interface may be used to manipulate the datasetand its subsets of data, including derived subsets of data. Deriveddataset attributes, derived data, and derived data arrangements (e.g.,derived columns) may be used to facilitate linking to other datasets,including protected datasets. Additional user interface elements, suchas a “link” user input 137 of FIG. 1, may be presented on a userinterface to facilitate linking of atomized datasets based onannotations associated with derived dataset attributes (e.g., responsiveto input from a data practitioner in view of insight information,dataset activity feed information, etc.).

At 1610, a second input may be accepted via the user interface (e.g., inassociation with a processor) using a second user interface element tocreate an atomized dataset. In some cases, the second user interfaceelement may be presented as the annotation, which, in turn, may beassociated with a column of data. Thus, activation of the second inputmay be configured to cause linking between the atomized dataset and toanother dataset based on the annotation. In other examples, the secondinput may be configured to invoke, upon activation, other dataoperations and functions.

FIG. 17 is a diagram depicting an example of a collaboration managerconfigured to present collaborative information regarding collaborativedatasets, according to some embodiments. Diagram 1700 depicts acollaboration manager 1760 including a dataset attribute manager 1761,and coupled to a collaborative activity repository 1736. In thisexample, dataset attribute manager 1761 is configured to monitor updatesand changes to various subsets of data representing dataset attributedata 1734 a and various subsets of data representing user attribute data1734 b, and to identify such updates and changes. Further, datasetattribute manager 1761 can be configured to determine which users, suchas user 1708, ought to be presented with activity data for presentationvia a computing device 1709 in a user interface 1718. In some examples,dataset attribute manager 1761 can be configured to manage datasetattributes associated with one or more atomized datasets. For example,dataset attribute manager 1761 can be configured to analyzing atomizeddatasets and, for instance, identify a number of queries associated witha atomized dataset, or a subset of account identifiers (e.g., of otherusers) that include descriptive data that may be correlated to theatomized dataset. To illustrate, consider that other users associatedwith other account identifiers have generated their own datasets (andmetadata), whereby the metadata may include descriptive data (e.g.,attribute data) that may be used to generate notifications to interestedusers of changes, modifications, or activities related to a particulardataset. The notifications may be generated as part of an activity feedpresented in a user interface, in some examples.

Collaboration manager 1760 receives the information to be presented to auser 1708 and causes it to be presented at computing device 1709. As anexample, the information presented may include a recommendation to auser to review a particular dataset based on, for example, similaritiesin dataset attribute data (e.g., users interested in Zika-based datasetsgenerated in Brazil may receive recommendation to access a dataset withthe latest dataset for Zika cases in Sao Paulo, Brazil). Note the listedtypes of attribute data monitored by dataset attribute manager 1761 arenot intended to be limiting. Therefore, collaborative activityrepository 1736 may store other attribute types and attribute-relatedthan is shown.

FIG. 18A depicts an example of a dataset attribute manager configured togenerate data to enhance datasets, according to some examples. Diagram1800 depicts a dataset attribute manager 1861 and one or more of itsconstituent elements may be configured to correlate, identify, analyze,and summarize datasets and dataset interactions, including correlating,identifying, analyzing, and summarizing user datasets, groups of otheruser datasets, groups of non-user datasets (e.g., datasets external tocollaborative dataset consolidation system), etc. Correlations anddataset interaction summary data may be fed via a dataset activity feedto disseminate dataset-related information via computing device 1802 ato user 1801 a, as well as via other computing devices (not shown) toother users (not shown). Examples of dataset interaction summary datainclude trending dataset information, trending user information,relevant dataset information, relevant collaborator, dataset trackinginformation, collaborator tracking information, and the like. Datasetattribute manager 1861 may be configured to calculate correlations amongdatasets and dataset interactions and may be further configured tosummarize the interactions for presentation via, for example, a datasetactivity feed. Dataset interactions may be represented as dataspecifying whether a particular dataset of has been queried, modified,shared, accessed, created, etc., or an event in which another usercommented on a dataset or received a comment for a dataset. Otherexamples may include data representations via a user interface thatconveys a number of queries associated with a dataset, a number ofdataset versions, identities of users (or associated user identifiers)who have analyzed a dataset, a number of user comments related to adataset, the types of comments, etc.). Data practitioners, therefore,may gain additional insights into whether a particular dataset may berelevant based on electronic social interactions among datasets andusers. Thus, at least some implementations may be described as “anetwork for datasets” (e.g., a “social” network of datasets and datasetinteractions).

Dataset attribute manager 1861 is configured to identify user datasetattribute data 1804 and user attribute data 1806 associated with a user1801 a via computing device 1802 a. Further, dataset attribute manager1861 may be configured to receive, identify, derive or determine globaldataset attribute data 1810 from a pool of dataset data, which mayinclude data representing a community of datasets, a subset of dataset,a superset of datasets (e.g., including all or substantially alldatasets identifiable or accessible by dataset attribute manager 1861),as well as interactions therebetween. Also, dataset attribute manager1861 may be configured to receive, identify, derive or determine globaluser attribute data 1820 for any number of users, user accounts, etc.Global user attribute data 1820 includes user-related and useraccount-related data over a community of users.

According to some embodiments, user attribute data 1806 may includeuser-related characteristics describing a user, a user account, a usercomputer device system, and other user-related characteristicsspecifying user interactions with any aspect of a collaborative datasetconsolidation system or external thereto. Examples of user attributedata 1806 include, but are not limited to, a geographic location (e.g.,of a user, a user computing device, etc.); a user identifier (e.g., auser name, a user account number identifier, etc.); demographicinformation; a field of interest and/or scientific discipline (e.g.,applied mathematics or engineering, chemistry, physics, earth sciences,astronomy, biology, social science, etc.); a profession and/or title;ranking of user 1801 a by others (e.g., others in a subset of users,such as user in an organization, a scientific discipline, a country,etc.), etc. Other examples of user attribute data 1806 include, but arenot limited to, data in datasets; dataset-related data (e.g., metadata),such as data representing tags; data representing titles and/ordescriptions of datasets; data indicating whether the dataset is open,private, or restricted; types and amounts of queries against datasetsowned by user 1801 a; types and amounts of queries by user 1801 aagainst other datasets; types and amounts of accesses of datasets ownedby user 1801 a; types and amounts of accesses by user 1801 a againstother datasets; types of users other datasets with whom user 1801 acollaborates; a number or type of users monitoring (e.g., “following”)datasets interactions by user 1801 a; a number or type of user ordataset interactions that user 1801 a follows or monitors; a number ortype of comments associated with datasets owned by user 1801 a; a numberor type of comments applied to datasets owned or managed by user 1801 a;a number of citations of one or more datasets owned or managed by user1801 a; a number of citations to one or more other datasets by user 1801a, and the like. Note that the above-identified user attribute data 1806are examples are not intended to be limiting. Note, too, one or more ofthe above-identified user attribute data 1806 may be used to determine a“context” of dataset usage, and may be referred to, or implemented as,dataset attributes.

Global dataset attribute data 1810 includes any number of datasets andsubsets of dataset attributes 1828 a, 1828 b, 1828 c, and 1828 n, one ormore of which may be associated with at least one dataset. Subsets ofdataset attributes 1828 a, 1828 b, 1828 c, and 1828 n may beuniquely-identifiable and accessible via, for example, a collaborativedataset consolidation system. Hence, data representing datasetattributes for subsets of dataset attributes 1828 a, 1828 b, 1828 c, and1828 n may be similar to those included in user dataset attribute data1804, such as described above.

According to some embodiments, user dataset attribute data 1804 mayinclude dataset-related characteristics describing data of a dataset,metadata or other data associated with a dataset, and otherdataset-related characteristics specifying dataset interactions with anyaspect of a collaborative dataset consolidation system or externalthereto. Examples of user dataset attribute data 1804 include, but arenot limited to, data representing quantities or types of links fromdatasets to a dataset of user 1801 a; data representing quantities ortypes of links from a dataset of user 1801 a to multiple datasets; datarepresenting context and/or usage of a dataset (e.g., categorical andnumeric descriptions thereof); data representing a number of accesses inassociation with a dataset (including views or any data interaction witha dataset); data representing a quantity of copies, downloads,modifications, or versions of a dataset; data representing types andquantities of comments associated with the dataset; data representingquantity of votes or ranking for one or more datasets; and the like.Additional examples of user dataset attribute data 1804 include, but arenot limited to, a number of distinct data points in a column of data, anumber of non-empty cells, a mean value, a minimum value, a maximumvalue, a standard deviation value, a value of skewness, a value ofkurtosis, as well as any other statistical or characteristic data. Notethat the above-identified user dataset attribute data 1806 are examplesare not intended to be limiting. Note, too, one or more of theabove-identified user dataset attribute data 1806 may be used todetermine a “context” of dataset usage, and may be referred to, orimplemented as, dataset attributes.

Global user attribute data 1820 may include any number of subsets ofuser attributes 1838 a, 1838 b, 1838 c, and 1838 n, one or more of whichmay be associated with at least one user or user account. Subsets ofuser attributes 1838 a, 1838 b, 1838 c, and 1838 n may be accessiblevia, for example, a collaborative dataset consolidation system. Hence,data representing user attributes in user attributes 1838 a, 1838 b,1838 c, and 1838 n may be processed similar to those included in userattribute data 1806, such as described above.

Dataset attribute manager 1861 is shown to include a trend datasetcalculator 1862, a relevant dataset calculator 1864, and a datasettracker 1866. Trend dataset calculator 1862 may be configured todetermine values (e.g., normalized values, such as standardized values,including z-score determination, or the like) of aggregated datasetattributes (e.g., over a large number of subsets of dataset attributes)with which to compare to a subset of dataset attributes for a dataset todetermine, for example, trending information or a rank for the dataset.For example, a particular dataset may have one or more datasetattributes with the following values or representations: a tag “Zika” asa data attribute, a number of 15,891 queries per unit time, and a numberof 389 requests to access the dataset. Thus, trend dataset calculator1862 may be configured to compare these values against, for example,“average” values, which may be calculated by trend dataset calculator1862. Further, trend dataset calculator 1862 may be configured tocompare “Zika” tags, “15,891” queries, and “369” requests to the averagevalues of the aggregated dataset attributes to determine a comparativeranking and/or trend information of the dataset. The comparative rankingand/or trend information may be transmitted via notifications to userinterfaces and activity feed portions thereof to disseminatedataset-related updates in real time (or substantially in real-time).Note that trend dataset calculator 1862 (as well as other calculatorsand logic of dataset attribute manager 1861) is not limited toimplementing an “average value” as a comparative value for aggregateddataset attributes, but rather, any other representation, metric (e.g.,mean value, etc.), or technique (e.g., use of k-NN algorithms,regression, Bayesian inferences and the like, classification algorithms,including Naïve Bayes classifiers, or any other statistical or empiricaltechnique) to measure or rank a dataset attribute relative to apredominated number of datasets may be implemented, according to variousexamples. Trend dataset calculator 1862 may generate trending datasetdata 1812 to initiate presentation of trending or ranking information ona user interface.

A superset of datasets associated with a superset of users, such as user1801 a, may be associated with global dataset attribute data 1810, whichmay include aggregated values or indicative values of a predominantnumber of datasets. For example, each subset 1828 a, 1828 b, 1828 c, and1828 n of dataset attributes may include data representative of anaggregated value (e.g., a normalized value) or representation of adataset attribute for the superset of datasets (e.g., large numbers ofdatasets associated with large numbers of users, including, but notlimited to all accessible datasets, etc.). As an example, consider thatsubset 1828 a of dataset attributes includes attribute data describing“an average number of links” to other datasets, subset 1828 b of datasetattributes includes attribute data describing “an average number oftimes datasets are returned as a result” of a search, subset 1828 c ofdataset attributes includes attribute data describing “an average numbercomments received” in association with datasets, and subset 1828 n ofdataset attributes includes attribute data describing “an average numberof times datasets are queried.” Based on these “average” values, whichmay be generated by trend dataset calculator 1862, dataset attributesvalues of associated one or more datasets may be compared to the averagevalues of the one or more datasets to determine a comparative rankingand/or trend information, as determined by trend dataset calculator1862. According to some embodiments, subsets 1828 a, 1828 b, 1828 c, and1828 n of dataset attributes that are identified as corresponding to asuperset of users may be used to determine a trend for, or a ranking of,a dataset for a particular user, such as user 1801 a relative to theaggregated values or representations of dataset attributes associatedwith the superset of datasets.

Relevant dataset calculator 1864 of dataset attribute manager 1861 maybe configured to determine values of dataset attributes of a particulardataset in global dataset attribute data 1810. Those attributes then canbe compared to corresponding similar dataset attributes of user datasetattribute data 1804 to determine, for example, a degree of relevancybetween two datasets. By determining a degree of relevancy between adataset of user 1801 a and another dataset associated with globaldataset attribute data 1810, relevant dataset calculator 1864 cangenerate relevant dataset data 1814 that represents a list of datasets1810 that may be most relevant to user 1801 a. Additionally, relevantdataset calculator 1864 may be configured to determine degrees ofrelevancy for any number of different datasets, whereby the mostrelevant datasets may be included in an activity feed for a user who maybe interested in such datasets. Therefore, user 1801 a may identify orlearn of a dataset of interest in an expedited manner than otherwisemight be the case.

In at least one other example, a user 1801 a (or a group ofcollaborators including user 1801 a) may correspond in association withsubsets 1828 a, 1828 b, 1828 c, and 1828 n of dataset attributes. As avariation of a previous example, consider that the following is relevantto a dataset: subset 1828 a of dataset attributes includes attributedata describing “a number of links” to other datasets, subset 1828 b ofdataset attributes includes attribute data describing “a number of timesa dataset is returned as a result” of a search, subset 1828 c of datasetattributes includes attribute data describing “a number commentsreceived” in association with a dataset, and subset 1828 n of datasetattributes includes attribute data describing “a number of times adataset has been queried.” According to some embodiments, subsets 1828a, 1828 b, 1828 c, and 1828 n of dataset attributes may be identified todetermine a degree of relevancy to, for example, user dataset attributedata 1804 to determine whether dataset attributes 1828 a, 1828 b, 1828c, and 1828 n may be relevant to the interests of user 1801 a.

Dataset tracker 1866 of dataset attribute manager 1861 maybe configuredto monitor and track new, updated, modified, deleted, etc. values ofdataset attributes of global dataset attribute data 1810, as well asuser dataset attribute data 1804. Thus, dataset tracker 1866 may beconfigured to determine updates for disseminating via dataset updatedata 1816 to relevant datasets, users, user accounts, etc. Thus, user1801 a may be notified to identify or learn of a particular datasetinteraction that facilitates the update to a dataset of interest. User1801 a then can explore the updates in an expedited manner thanotherwise might be the case

Dataset attribute manager 1861 also is shown to include a trend usercalculator 1863, a relevant collaborator calculator 1865, and acollaborator tracker 1867 that are configured to generate trending userdata 1811, relevant collaborator data 1813, and collaborator update data1815, respectively. According to some examples, trend user calculator1863, relevant collaborator calculator 1865, and collaborator tracker1867 may be configured to perform similar or equivalent functions astrend dataset calculator 1862, relevant dataset calculator 1864, anddataset tracker 1866, respectively, but using user attribute data 1806and global user attribute data 1820. Therefore, trend user calculator1863 may be configured to determine values of aggregated user attributes(e.g., over a large number of subsets of user attributes) with which tocompare to a subset of user attributes for a dataset to determine, forexample, trending information or a rank for a user based on global userattribute data 1820. As such, trend user calculator 1863 may beconfigured to determine, for example, a subset of the most highly-rankedusers for a unit of time. An electronic notification may be transmittedvia trending user data 1811 to a user interface of computing device 1802a to notify user 1801 a of trending users.

Relevant collaborator calculator 1865 may be configured to determinevalues of user attributes of a particular dataset in global userattribute data 1820 with which to compare to corresponding similar userattributes of user attribute data 1804 to determine, for example, adegree of relevancy between two users or user accounts, or the like. Bydetermining a degree of relevancy between user 1801 a and another userassociated with global user attribute data 1820, relevant collaboratorcalculator 1865 can generate relevant collaborator data 1813 forpresentation as a notification in a user interface of computing device1802 a. Relevant collaborator data 1813 may present to user 1801 a themost relevant collaborators (e.g., a top 10 list of relevant users) sothat user 1801 a may determine whether to collaborate, or otherwiseexchange data electronically with another user to facilitateimprovements in the datasets or efforts of user 1801 a. Collaboratortracker 1867 may be configured to determine updates to changes in userattributes for disseminating via collaborator update data 1815 torelevant datasets, users, user accounts, etc. (including user 1801 a) toalert interested entities of user interactions that may be of interest.

FIGS. 18B and 18C are diagrams that depict examples of calculators todetermine trend and relevancy data relating to collaborative datasets,according to some examples. Diagram 1850 of FIG. 18B depicts a trenduser calculator 1863 configured to receive user attributes 1830 of aparticular user 1840. Trend user calculator 1863 also is configured toreceive aggregated user attributes 1834 associated with a pool of users1842, and data representing a number of weighting factors 1832 toinfluence operations of trend user calculator 1863. While in some cases,aggregated user attributes 1834 may represent average or mean values forrespective attributes, this need not be required. Thus, the values ofaggregated user attributes 1834 can be any value for comparing datasetor user attributes to each other. Regardless, each of aggregated userattributes (e.g., UA1, UA2, . . . , UAn) may have a value with which tocompare with values of user attributes 1830 to determine a relativeranking of user 1840 in view of pool of users 1842. Values of weightingfactors 1832 are configurable to emphasize or deemphasize one or moreuser attributes in determining trending user data 1811. For example,user attributes associated with any of “tags,” scientific discipline(“Sci Displ”), and “country” may be emphasized or deemphasized based onweighting factors 1832.

Trend user calculator 1863 is shown to also include differential modules1831 configured to detect a value of a user attribute 1830 anddetermine, for example, a “distance” to a value of an aggregated userattribute 1834. In cases in which the value of user attribute 1830closely matches, or is near to, a value of an aggregated user attribute1834, the value of user attribute 1830 is less notable, and, therefore,less likely to be indicative of a trend beyond, for example, the norm.But in cases in which the value of user attribute 1830 divertsrelatively significantly from an aggregated user attribute 1834, thenuser attribute 1830 and the associated dataset may be of greaterinterest to certain users and datasets. For example, if the value ofuser attribute 1830, such as a number of “followers,” diverges from anaverage value of “followers,” then trending data may indicate that anincreased number of other users have requested to “follow” datasetinteractions associated with a particular user or dataset. Suchincreases may indicate that the highly-followed dataset may be perceivedas being of high-value to a community of data practitioners. Trend usercalculator 1863 can transmit trending user data 1811 to notifypotentially interested users in activity feeds. Therefore, potentiallyinterested users may learn quickly of new developments in data sciencemanagement analytics in real-time or near real-time.

Diagram 1870 of FIG. 18C depicts a relevant collaborator calculator 1865configured to receive user attributes 1880 of a particular user 1890.Relevant collaborator calculator 1865 also is configured to receive userattributes 1884 associated with a user under analysis 1892 (e.g., a userbeing analyzed to determine whether the user is relevant to user 1890),and data representing a number of weighting factors 1882 to influenceoperations of relevant collaborator calculator 1865. While in somecases, user attributes 1884 may represent average or mean values forrespective attributes, this need not be required. Thus, the values ofuser attributes 1884 can be any value for comparing dataset or userattributes to each other. So, each of user attributes 1884 may have avalue with which to compare with values of user attributes 1880 todetermine a relative relevancy of user under analysis 1892 to user 1890.Values of weighting factors 1882 are configurable to emphasize ordeemphasize one or more user attributes in determining relevantcollaborator data 1813.

Relevant collaborator calculator 1865 is shown to also includedifferential modules 1881 configured to detect a value of a userattribute 1880 and determine, for example, a “distance” to a value ofuser attribute 1884. Unlike the above-described operation ofdifferential modules, when a value of user attribute 1880 closelymatches, or is near to, a value of a user attribute 1884, the value ofuser attribute 1884 is indicative that user attribute 1880 and userattribute 1884 are similar (i.e., relevant) to each other relative toothers. For example, if the value of user attributes 1884 include a“tag” (e.g., Zika), a scientific discipline (“Sci Displ”) (e.g.,biology, including serology and vaccine development), and a “country”(e.g., U.S.A.), that closely match those of user attributes 1880, then adataset associated with user under analysis 1892 may be relevant to theefforts to user 1890. Relevant collaborator calculator 1865 can transmitrelevant collaborator data 1813 to notify potentially interested usersin activity feeds. Therefore, potentially interested users may learnquickly of new developments in data science management analytics inreal-time or near real-time and can contact or collaborate withnewly-found datasets.

FIG. 19 is a diagram depicting an example of a dataset activity feed topresent dataset interaction control elements in a user interface,according to some embodiments. Diagram 1900 depicts a user interface1902 presenting one or more user interface elements constituting useraccount information 1910, user datasets 1920, 1922, and 1924, and adataset activity feed 1950. One or more text strings depicted in userinterface 1902 may be configured as control elements (e.g., user inputs)that, in response to user interaction via activation of the user input,cause presentation of dataset interaction information. Activation may betriggered by, for example, selecting or hovering over a user input usinga pointer element 1990 (or other things, such as a finger on atouch-sensitive display).

User account information 1910 may include user interface elements 1972 aand 1972 b that relate to “a number of followers” and “a numberfollowing,” respectively. The “number of followers” may indicate anumber of other datasets or other user accounts that monitor datasetinteractions relating to datasets 1920, 1922, and 1924, as well asupdates to user attributes associated with user account information1920. For example, each of at least eleven (11) datasets may beindicated as a “follower” that may cause notifications to be receivedinto dataset activity feed 1950 of the user interfaces (not shown)associated with the eleven datasets (and other 11 users) if, forexample, dataset 1920 is modified, queried, or any other datainteraction are performed, or any other action is performed by a userassociated with user account 1910. By contrast, the “number following”may indicate a number of other datasets or other user accounts that useraccount 1910 and datasets 1920, 1922, and 1924 are following, and mayreceive notifications in dataset activity feed 1950. For example,pointer element 1990 a may select or hover over user interface element1972 b to cause presentation (not shown) of identities and otherinformation describing twelve (12) datasets being followed by datasets1920, 1922, or 1924 and/or user “Sherman” of user account 1910. Further,user interface elements associated with corresponding user datasets1920, 1922, or 1924, if activated, may be configured to display orotherwise cause dataset-related information, including datasetattributes, to be presented via user interface 1902 or any other userinterface.

Dataset activity feed 1950, in this example, are shown to include anumber of notifications 1951, 1952, 1953, 1954, 1955, 1956, and 1957,each having user interface elements to cause activation and/orpresentation of dataset-related interaction functions and/orinformation. Each of notifications 1951, 1952, 1953, 1954, 1955, 1956,and 1957 may be generated (e.g., remotely) in response to one of anumber of dataset interactions associated with a corresponding dataset.In at least some notifications, such as notification 1951, a type ofdataset interaction 1951 a may be identified in view of a useridentifier 1970 and a dataset 1972. Here, notification 1951 indicatesthat a user “Adam” associated with user identifier 1970 has caused adataset interaction 1951 a of “creating a dataset,” with a datasetidentifier (“Open Food Fact”) 1972 for a dataset. Notifications 1952,1953, 1954, 1955, 1956, and 1957 may be provide indications,respectively, of dataset interaction 1952 a (e.g., dataset “Age-CAP” isqueried by user “Beth”), 1953 a (e.g., dataset “Age-CAP” has accessgranted to Becky user “Adam”), 1954 a (e.g., dataset “Dementia SurveyData” is queried by user “Mo”), 1955 a (e.g., dataset “HistoricalTrading Data” is corresponds, or is linked to, dataset “Historical GPD”by user “JohnQ”), 1956 a (e.g., dataset “Historical GPD” is added oruploaded by user “JohnQ”), and 1957 a (e.g., dataset “Student Loan Data”has been associated with a comment added by user “Sid”). Other datasetinteractions are possible.

Further, user interface elements of notifications 1951 to 1957 may beactivated to at least present related information. For example, pointerelement 1990 b may select user identifier 1970 (e.g., a hyperlink orcontrol input) that may cause presentation of user account-relatedinformation for user “Adam” (not shown). As another example, pointerelement 1990 c may be activated to select data interaction 1957 a tocause generation of an overlay window to present, for example, text 1992of a comment. Therefore, a user may determine another user, a comment,and a dataset “in a view” (e.g., single user interface view), whereby auser may expedite data collection and collaboration.

In view of the foregoing, collaboration among users and formation ofcollaborative datasets may be expedited based on the disseminationup-to-date information provided by dataset activity feed 1950. Thus,user “Sherman” 1910 more readily may be able to determine applicabilityof other datasets, such as dataset “Dementia Survey Data” ofnotification 1954, to one of user datasets 1920, 1922, and 1924.Consequently, user “Sherman” 1910 may expedite modeling data and/ortheory testing, among other things.

FIG. 20 is a diagram depicting other examples of dataset activity feedsto present a dataset recommendation feed in a user interface, accordingto some embodiments. Diagram 2000 depicts a user interface 2002presenting one or more user interface elements of one or more datasetrecommendation feeds based on dataset interactions and datasetattributes. User interface 2002 is shown to include a datasetrecommendation feed 2020 that presents a number of higher-ranked “tags”2010 that may be of interest (and relevant) to a dataset and useraccount. Also, user interface 2002 may include a dataset recommendationfeed 2022 that presents a number of users as “who to follow,” each ofwhom may be of interest (and relevant) to a dataset and user account. Auser may be follow another user (e.g., initiate receipt ofnotifications) by selecting a corresponding user input 2012. Further,user interface 2002 may include a dataset recommendation feed 2024 thatpresents a number of discussions regarding, for example, a dataset 2014,such as dataset “Hate Crime Laws and Statistics.” One or more textstrings depicted in user interface 2002 may be configured as controlelements (e.g., user inputs) that, in response to user interaction viaactivation of the user input, cause presentation of respective datasetrecommendation information.

FIG. 21 is a diagram depicting examples of trend-related datasetactivity feeds to facilitate presentation and interaction with userinterface elements, according to some embodiments. Diagram 2100 depictsa user interface 2102 configured to present user interface elements,which may be interactive (e.g., control user inputs), that constitutetrending information. Trending dataset-related information 2120 is shownto include trending datasets 2130 and trending users 2140.

Trending datasets 2130 may be disposed in a portion of user interface2102 that presents user interface elements including, for example, aranking 2132 (e.g., ranking of 1, 2, 3, . . . , n). Rankings 2132 areeach associated with data representing a trending dataset 2150, whichmay include other user interface elements, such as text information 2152describing trending dataset 2150. An exemplary description may include apurpose of the dataset, a source of the dataset, a field ofapplicability (e.g., biological, such as serological applications totest vaccines, etc.), and the like. Trending datasets 2150 may alsoinclude an indication 2156 whether the dataset is open, restricted, orprivate. Further, trending datasets 2150 may also include user interfaceelement 2158, which may be a control element (e.g., a user input). Onactivation, such as by a pointer element, user interface element 2107may be configured to generate an overlay window 2110 (over interface2102). Overlay window 2110 may include an interface to initiate arequest to access trending datasets 2150 via user input 2171, or toreject linking trending dataset 2150 to a dataset 2104. As trendingdataset 2150 is a private dataset, a username field 2175 and passcodefield 2177 may be presented in overlay window 2110 to facilitateauthentication for accessing or linking to trending dataset 2150.

Trending users 2140 may be disposed in another portion of user interface2102 and may present user interface elements including, for example, aranking 2142 (e.g., ranking of 1, 2, 3, . . . , n). Trending user 2160may have similar user interface elements as trending dataset 2150, suchas a text description 2162 and a user interface element 2168 configuredto form a link between user 2105 and trending user 2160. In someexamples, activation of link element 2168 may cause, for example, datato be exchanged (e.g., notifications) among users, as well as may enablea number of permissions when accessing a dataset associated withtrending user 2160. Permissions include, but are not limited to,authorization to copy a dataset, authorization to modify a dataset,authorization to query a dataset, etc.

FIG. 22 is a diagram depicting other examples of relevancy-relateddataset activity feeds to facilitate presentation and interaction withuser interface elements, according to some embodiments. Diagram 2200depicts a user interface 2202 configured to present user interfaceelements, which may be interactive (e.g., via control user inputs), thatconstitute relevancy information. Relevant dataset-related information2220 is shown to include relevant datasets 2230 and relevant users 2240,which may include user interface elements similar to trending datasets2130 and trending users 2140, respectively, of FIG. 21. A portion ofuser interface 2202 that includes relevant datasets 2230 may beconfigured to present, for example, highest ranked user datasets 2250that rank in accordance with their degree of relevancy to dataset 2204(e.g., ranking “1” of rankings 2232 indicates the most relevant otheruser dataset). Relevant datasets 2230 may include user interfaceelements, such as “private” indication 2256, and a user interfaceelement 2258 (e.g., a control element as a user input) configured toactivate an overlay interface (not shown) to link relevant dataset 2250to dataset 2204. Relevant users 2240 may include a user interfaceelement 2268 configured to establish a link between relevant dataset2250 and user 2205. Another portion of user interface 2202 includesrelevant users 2240 that is configured to present, for example, highestranked users 2260 that rank in accordance with their degree of relevancyto user 2205 (e.g., ranking “1” of rankings 2242 indicates the mostrelevant other user). In some examples, user interface 2202 may includeuser interface elements, such as user input 2295, for voting orotherwise expressing usefulness of dataset 2204. For example, user input2295 may indicate a number of users or viewers that “like” dataset 2204.User input 2297 may be implemented to cause information regardingdataset 2204 to be transmitted (i.e., communicated) to third partysocial networking systems, such as Facebook™, Twitter™, and the like.According to some examples, elements depicted in diagram 2200 of FIG. 22may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

FIG. 23 is an example of a data entry interface to access atomizeddatasets, according to some examples. Diagram 2300 depicts acollaborative dataset access interface 2322, which operates as acomputerized tool to access a collaborative dataset (e.g., an atomizeddataset) or perform other operations, such as a query. Also shown is auser interface element generator 2380, a programmatic interface 2390,and a collaborative dataset, consolidation system 2310, which, in turn,includes a dataset attribute manager 2361 and a repository 2340.According to some examples, elements depicted in diagram 2300 of FIG. 23may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings. Collaborativedataset access interface 2322 includes a data entry interface 2350 toenter, for example, commands to access a graph data structure (e.g., anatomized dataset). A graph-level access or query language, such asSPARQL, or the like, may be used to enter a query into data entryinterface 2350. When SPARQL is used, a “SPARQL” indicator 2303 may bepresented. In this example, a query having a title (“First 100 DataPoints”) 2305 is entered into data entry interface 2350 for queryingagainst a linked dataset 2301. The query may be executed upon activationof user input (“run query”) 2390. Further, collaborative datasetconsolidation system 2310 may generate collaborator update data 2305 topropagate or disseminate notifications that a user has performed a queryagainst a dataset 2301. Also, collaborative dataset consolidation system2310 may generate dataset update data 2370 to alert other datasets orusers that a particular dataset has been queried. User input 2375 maycause a query to be published, which enables the query and results tobecome visible and shareable from, for example, a dataset homepage (notshown). Therefore, a community of data practitioners may be able keepinformed about developments or dataset interactions regarding datasetsin which they are interested.

FIG. 24 is an example of a user interface to present interactive userinterface elements to provide a data overview of a dataset, according tosome examples. Diagram 2400 depicts a user interface 2402 as an exampleof a computerized tool to provide access to summarized data, informationand aspects of a dataset (e.g., an atomized dataset), including schemainformation, or to perform other operations. In some examples, “insightinformation” may be calculated and presented via user interface 2402,which may be generated during a dataset creation process, orsubsequently thereafter, to present user interface elements that mayinclude, for example, characterizations of the data in summary formshown in diagram 2400. For example, user interface elements may presentinformation, (e.g., textually, statistically, graphically, etc.) thatmay convey characteristics of the data distribution and “shape,” amongother things. Diagram 2400 also depicts a user interface elementgenerator 2480, a programmatic interface 2490, and a collaborativedataset consolidation system 2410, and a data derivation calculator2465. According to some examples, elements depicted in diagram 2400 ofFIG. 24 may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

Insight information may be generated during at one or more phases of adataset creation process, or subsequently. “Insight information” mayrefer, in some examples, to information that may automatically convey(e.g., visually in text and/or graphics) dataset attributes of a createddataset, including derived dataset attributes, during or after (e.g.,shortly thereafter) the creation of the dataset. Insight informationpresented in a user interface (e.g., responsive to dataset creation) maydescribe various aspects of a dataset, in summary form, such as, but notlimited to, annotations (e.g., of columns, cells, or any portion ofdata), data classifications (e.g., a geographical location, such as azip code, etc.), datatypes (e.g., a string data type, a numeric datatype, a categorical data type, a Boolean data type, a derivedclassification data type, a date data type, a time data type, ageolocation data type, etc.), a number of data points, a number ofcolumns, a “shape” or distribution of data and/or data values, a numberof empty or non-empty cells in a data structure, a number ofnon-conforming data (e.g., a non-numeric data value in column expectinga numeric data, an image file, such as an image file embedded in a cell,etc., or some other erroneous or unexpected data) in cells of a datastructure, a number of distinct values, etc. According to someembodiments, initiation of the dataset creation process invoked at auser input of user interface 2402 (not shown) may also performstatistical data analysis during or upon the creation of the dataset.

In view of the foregoing, algorithms (e.g., statistical algorithms orother analytic algorithms) may be applied against the dataset, during orsubsequent to the creation of the dataset, to access insightinformation. A user need not download the data from a dataset to performsome sort of ad hoc data analysis (e.g., creating and running a Pythonscript, or the like, against downloaded data to perform a statisticalanalysis) to identify characteristics of a distribution of data as wellas visualization of the distribution. Therefore, insight informationpresented in user interface 2402 may provide (e.g., automatically)dataset attributes (and characteristics thereof) in a “snapshot view” sothat data practitioners and users may readily determine whether adataset may be of a quality or of a form that serves a desired purposeor objective. Further, user interface 2402 may automatically calculateand present dataset attributes of a “linked” dataset in which a link isestablished with, for example, an atomized dataset. In some examples,user interface 2402 may automatically convey dataset attributes, insummary form, for collaborative datasets that include links to protected(e.g., secured) atomized datasets that require authentication orpermission to access in a collaborative dataset.

In the example shown, user interface 2402 is configured to presentinsight information for a dataset 2404. User interface 2402 isconfigured to convey insight information for a subset of dataarrangements in a data arrangement overview interface 2411. In theexample shown, data arrangement overview interface 2411 is configured toprovide an overview (e.g., summary) or aggregation of, for instance,columnar data from a tabular data arrangement, such as an XLS file. Asshown, each data arrangement, or column, of a set of data 2405, which isdepicted as file “Earthquakes M4_5 and higher (30 Day Interval).xls,”may be presented as a row in data arrangement overview interface 2411,with each row depicting an aggregation (e.g., summarization) of dataattributes and thereof values.

As shown, a number of columns are depicted in orthogonal dataarrangements (e.g., as rows) wherein a column header (or annotation) isprovided in column (“column name”) 2420, which describes a datasetattribute of the data disposed therein. In this example, an index 2421,or ranking, is depicted adjacent to text describing a column annotation.Indices 2421 for columns 1 (“time”) to 6 (“magType”) are shown in dataarrangement overview interface 2411. Note that in this example, datapreview mode 2413 indicates that there are twenty-two (“22”) columns and355 rows that may be summarized and viewed with, for example, the use ofscroll bar 2414. Further, each column may include a datatype 2421indicating a datatype for the column, a number 2422 (e.g., inpercentage) of empty cells, and a number 2423 of distinct values for thecolumnar data, among many other types of data. In the example shown, asubset of column headers are disposed at 2420 a, 2420 b, 2420 c, 2420 d,2420 e, and 2420 f, each having column headers (e.g., annotations)“time,” “latitude,” “longitude,” “depth,” magnitude (“mag”), andmagnitude type (“magType”), respectively. Therefore, in this example,orthogonal arrangements of data associated with 2420 a, 2420 b, 2420 c,2420 d, 2420 e, and 2420 f may be configured to display, respectively,aggregate or summary information regarding the “time” of an earthquake,the latitude and longitude of an earthquake, a depth at which anearthquake originates, a magnitude of the earthquake and a magnitudetype of earthquake (e.g., “mb,” or measured body-wave magnitude), amongother dataset attributes (some of which are not shown and viewable via2414).

Data arrangement overview interface 2411 also may include a column 2424to present graphically a “shape” of the data for subsets of dataingested during uploading of file 2405. The shape and presentation ofthe data may be presented as a histogram, a line graph, a percentage, atop number of categorical values, or in any other visualizationgraphical format to convey summary information, visually, based onvalues of a subset of the dataset attributes for a column. Toillustrate, consider shape data 2424 a associated with a subset 2420 aof data that presents earthquake magnitude (“mag”) summary information.As shown, a histogram 2424 a depicts the frequencies of earthquakemagnitude ranging from a minimum magnitude 2492 of 4.5 (on the Richterscale) to a maximum magnitude 2494 of 7.8, with predominant frequenciesare shown nearer minimum magnitude 2492. Also shown, top categories 2424b depict the most common and next most common categories of earthquakemagnitudes. As shown, the most common category 2493, with 298occurrences, is the “mb” category, which describes a number ofoccurrences in which an earthquake magnitude may be categorized as ameasured body-wave magnitude. The next common category 2495, with 26occurrences, is the “mmw” category, which describes a moment magnitudederived from a centroid moment tensor inversion of a W-phase. The ‘mmw’category may be a very long period phase (e.g., 100 seconds to 1000seconds) that may be derived to provide rapid characterization of aseismic source for tsunami warning purposes.

According to some examples, one or more of subsets of column headersdisposed at 2420 a (“time”), 2420 b (“latitude”), 2420 c (“longitude”),2420 d (“depth”), 2420 e (magnitude, or “mag”), and 2420 f (magnitudetype, or “magType”) may be derived. For example, any of the columnsassociated with 2420 a to 2420 f may include one or more derived datasetattributes and associated values. For example, one of subsets of columnheaders disposed at 2420 a to 2420 f may include “place” (e.g., name ofa geographic location or city) derived from data in 2420 b (“latitude”)and 2420 c (“longitude”), as well as associated values (e.g., distancesto nearest cities to earthquake epi-centers). Another example of aderived column may be depicted in FIG. 13, among others. Any one of thecolumn headers disposed at one of 2420 a to 2024 f may be derived,whereby the column header may be associated with an annotation, whichmay be automatically provided or by the user. An annotation may bederived based on inferred or derived dataset attributes, and, as such, acolumn header (or any metadata associated with a subset of data in adataset), may be derived or inferred, as described herein. Therefore,insight calculations and user interface elements presented in dataarrangement overview interface 2411 may be based on a derived datasetattribute and/or associated values.

Data arrangement overview interface 2411 may be configured to functionas an interactive display, whereby a display of graphically-displayeddistributions 2424 of data, such as a histogram, may include userinterface elements (e.g., control inputs or user inputs) that facilitateinteraction with presented data, including some data representingderived or inferred dataset attributes. To illustrate, consider that apointer element 2447 b may be configured to select or hover over aportion of the histogram associated with the maximum magnitude value. Inresponse, data arrangement overview interface 2411 may present anoverlay window (not shown) that provides additional information aboutearthquake magnitudes at 7.8 on the Richter scale (e.g., the overlaywindow may provide information as to geographic location, a time, anaffected country or city, etc.).

Furthermore, data arrangement overview interface 2411 may include userinterface elements 2443 and 2445 to provide enhanced control of at leasta portion of a data creation process, as described herein. “Link” userinput 2443 may be configured to initiate selection of another datasetwith which to link to the present dataset 2404 to form a collaborativedataset. Consequently, logic of one or more of user interface elementgenerator 2480, a programmatic interface 2490, and a collaborativedataset consolidation system 2410, and a data derivation calculator 2465may be configured to re-calculate insight information based on acombination of data from the other set of data linked to the set of data2405 to derive updated dataset attributes and associated updated values(e.g., updated aggregated dataset attributes and associated updatedvalues). In some examples, “Link” user input 2443 may be configured tolink a protected atomized dataset to present dataset 2404. Authorizationdata to access the protected atomized dataset may accompany controlsignals associated with user input 2443 to facilitate access to theprotected atomized dataset to perform recalculations of the insightinformation. Thus, the values for the insight information may berecalculated and adapted to new versions of collaborative datasetsduring the linking (e.g., uploading) phase for presentation of anupdated shape or distribution information for the combination of datathat may be presented in column 2424.

“Add files” user input 2445 may be activated via pointer element 2447 ato initiate adding (e.g., uploading) files to provide additionaldatasets or to correct data in set of data 2405. Thus, a new version ofa file 2405 may be uploaded to form a new version of dataset 2404. Forexample, “depth” column insight information 2475 indicates that 5 of 355rows (e.g., 1.4%) include “empty” data cells. A user may download thedata file 2405 and address the empty data cells by correcting the datatherein or adding appropriate data. Then, the revised file 2405 may beuploaded (e.g., via “add files” input 2445) so as to initiatere-calculation of the insight information, whereby “depth” columninsight information 2475 may be revised to include “zero” empty cells(not shown). The foregoing implementations of presenting insightinformation are examples and are not intended to be limiting, and thereare many variations that fall within the scope of the presentdisclosure. In some examples, a pointer element 2447 c may be configuredto select or hover over a portion 2489 of user interface 2402 to cause atransition to, or display of, a data overview 2511 of FIG. 25. Portion2489 may include hyperlinked text “Switch to data preview,” as anexample.

FIG. 25 is an example of a user interface to present interactive userinterface elements for another data preview of a dataset, according tosome examples. Diagram 2500 depicts a user interface 2502 as an exampleof a computerized tool to provide access to a portion of the dataset topresent a data view 2511 as a portion of a dataset. According to someexamples, data view 2511 presents the data in a tabular format. Hence,logic (not shown) may be configured to upload and ingest, during adataset creation process, a set of data 2505 formatted in, for example,a comma separated value, or “CSV,” format. The logic may be configuredformat the data for presentation via data view 2511 into a tabularformat as depicted in diagram 2500, with cells (e.g., intersections of arow and a column, other than column headers, indices, etc.) includingspecific values of data. As shown, subsets of dataset data are disposedin columns 2520 (“time”), 2522 (“latitude”), 2523 (“longitude”), 2524(“depth”), 2525 (magnitude, or “mag”), and 22526 (magnitude type, or“magType”). Diagram 2400 of FIG. 24 is an example of summary informationof the data presented in data view 2511. “See all” text 2580 mayhyperlinked to cause, upon activation, presentation of rows 1-355 andcolumns 1-22, which is beyond that shown in data view 2511. Further,user interface elements of user interface 2502 may include a user input2589 (e.g., a link, or hyperlink) in association with text “Switch tocolumn overview.” Activation of user input 2589 may cause thepresentation of data preview 2511 to transition to user interface 2402or data arrangement overview interface 2411 of FIG. 25.

FIG. 26 is a diagram depicting a flow diagram to present interactiveuser interface elements for a data overview of a dataset, according tosome embodiments. Flow 2600 may be an example of generating insightformation for a created dataset to present to a user interface based ona set of data. At 2602, data to form an input as a user interfaceelement may be received via a user interface. For example, a processorexecuting instruction data at a client computing device (or any othertype of computing device, including a server computing device) mayreceive data to form a user interface element, which, upon activation,may initiate creation of an atomized dataset based on raw data in datafile (e.g., a tabular data file, such as an XLS file, etc.). At 2604,which is optional, a programmatic interface may be activated tofacilitate the creation of (or updating or modifying) a datasetresponsive to receiving the first input, according to some examples.Insight information may be calculated during a phase, such as during a“Gathering Insights” 1024 phase of FIG. 10. Referring back to FIG. 26, aprogrammatic interface at 2604 may be implemented as either hardware orsoftware, or a combination thereof. The programmatic interface also maybe disposed at a client computing device or a server computing device,which may be associated with a collaborative dataset consolidationsystem, or may distributed over any number of computing devices whethernetworked together or otherwise. In some examples, the programmaticinterface may be distributed as subsets of executable code (e.g., asscripts, etc.) to implement APIs in any number of computing devices. Insome embodiments, programmatic interface may be optional and may beomitted.

At 2606, data may be received, for example, at a processor, and the datamay be a result of insight calculations. Further, the resultant data maydescribe a portion of insight information regarding dataset attributesof the dataset. In some examples, the insight information may becomputed based on, for example, a derived or inferred dataset attributeand/or associated values. At 2608, a set of data (e.g., a CSV file) maybe transformed during an ingestion process into a particular format,such as into an atomized datasets. At 2610, a data arrangement overviewinterface summarizing the data attributes as an aggregation of dataattributes in a portion of a user interface. Examples are depicted inFIGS. 24 and 25, among other drawings. The data arrangement overviewinterface may include an interactive display of a distribution of asubset of values for a data arrangement associated with a collaborativeatomized dataset. In some examples, an interactive display and/or anested overlay window may include an overlay interface (e.g., includinga tool tip) in which summary insight information may be presentedresponsive to interactions with the user interface.

FIG. 27 is an example of a user interface to present interactive userinterface elements for conveying summary characteristics of a dataset,according to some examples. Diagram 2700 depicts a user interface 2702as an example of a computerized tool to provide access to various levelsof detail for summarized data, information and aspects of a dataset(e.g., an atomized dataset). In the example shown, user interfaceincludes a data arrangement overview interface 2711, which, may, in atleast some cases, have structures and/or functionalities similar orequivalent to data arrangement overview interfaces described herein.According to some examples, elements depicted in diagram 2700 of FIG. 27may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

Logic of one or more of a user interface element generator, aprogrammatic interface, a collaborative dataset consolidation system,and a data derivation calculator, none of which are shown, may beconfigured to facilitate interactivity of data arrangement overviewinterface 2711 with a pointer element, including a finger (e.g., fortouch-sensitive screens), such as pointer element 2747. In someexamples, logic disposed in a collaborative dataset consolidation systemor at a client computing device, or both, may be configured to determinesummary characteristics (e.g., statistical characteristics) as datasetattributes of a collaborative dataset. For instance, the logic can beconfigured to calculate summary characteristics, such as a mean of thedataset distribution, a minimum value, maximum value, a value ofstandard deviation, a value of skewness, a value of kurtosis, etc.,among any type of statistic or characteristic. Summary characteristicsand graphical representations of data distributions may be referred toas dataset attributes, according to some examples.

Pointer element 2747 may select or hover over a user interface element,such as user input 2730. In response, the logic may cause activation ofinteractive overlay window 2750. User input (“latitude”) 2730 may betext (or an area thereof) that when selected may activate a subset ofthe executable code to cause generation of interactive overlay window2750. As shown, user input 2730 may be associated with the text“latitude,” which may be a hypertext link or another type of controlinput for invoking interactive overlay window 2750. Further, interactiveoverlay window 2750 may be configured to present data representingsummary characteristic data for subsets of data, such as columns ofdata. As shown, user input (“latitude”) 2730 is associated with a subsetof dataset data that relates to a column of latitude data in, forexample, a tabular representation of dataset 2704, which may be anatomized dataset. As pointer element 2747 selects or hovers over userinput 2730, a subset of data relating to “latitude” is identified,interactive overlay window 2750 is activated, thereby presenting summarycharacteristics for the subset of data, which is shown to directed tocolumn 2 (“col 2”) having annotation (“latitude”) 2751. Annotation 2751may be derived from a column header.

Interactive overlay window 2750 may be configured to present anannotation 2751, a datatype (“numeric”) 2752 for the subset of data, agraphical representation of a data distribution 2790 (e.g., ahistogram), and aggregated data attributes 2755 including summarycharacteristics. In this example, a column of latitude data may have thefollowing summary characteristics: distinct number of latitude values(“354”) 2760, a number of non-empty data fields or cells (“355”) 2761, anumber of empty data fields or cells (“0(0%)”) 2762, a mean of value ofthe latitude coordinates (“2.146”) 2763, a minimum value of the latitudecoordinates (“−63.586”) 2764, a maximum value of the latitudecoordinates (“85.597”) 2765, a standard deviation (“32.086”) 2766, avalue of skewness (“0.053”) 2767, a value of kurtosis (“−0.872”) 2768,among other statistical characteristics or any other summarycharacteristics.

According to some embodiments, interactive overlay window 2750 mayinclude user interface elements, as user inputs, to further perform dataoperations interactively with interactive overlay window 2750 todetermine yet another level of details. For example, interactive overlaywindow 2750 may include an interface to enter text or other symbols intothe interface. As shown, interface 2770 is configured to receive userinput to recalculate the above-described summary characteristics andgraphical representation 2790 based on adding or omitting datasets (orportions thereof). User input 2771 may be activated to add a particulardataset (e.g., add dataset “X”), whereas user input 2773 may beactivated to remove a dataset (e.g., remove dataset “Z”).

As another example, interactive overlay window 2750 may include one ormore user interface elements to cause presentation of a nested overlaywindow 2792. Pointer element 2747 may transition to another position onuser interface 2702, such as to a position depicted as pointer element2748. At this position, pointer element 2748 may be configured to causeidentification (e.g., through selection or hovering over) of a bar 2753of graphical representation 2790, which is shown as a histogram.Responsive to pointer element 2748, nested overlay window 2792 may begenerated to present relevant values of dataset attributes, such as atotal number latitude data points (e.g., 22 latitude coordinates) and arange of latitude values (e.g., from 38.356 to 40.842) associated withbar 2753. As shown, histogram 2790 has a number of bars representing afrequency in which a latitude data value falls within a particular rangeof latitude data values. The above-described examples are not intendedto be limiting, and an interactive overlay window and/or a nestedoverlay window may include any type of data and/or control inputs (asuser inputs). And in view of the foregoing, a user need not download adataset to perform ad hoc data analysis, such as creating and running aPython script at a client computing device against downloaded data todetermine applicability. Therefore, such information may be presentedvia interactive overlay window 2750.

FIG. 28 is a diagram depicting a flow diagram to present summarycharacteristics for a dataset in an interactive overlay window,according to some embodiments. At 2802, data representing summarycharacteristic data for subsets of data may be presented in a userinterface. A summary characteristic may be a statistic or any other datacharacteristic or dataset attribute, whereby a subset of data may referto a column of data in some examples. At 2804, a user interface elementmay be selected. An example of the user interface element may be a userinput configured to activate presentation of an interactive overlaywindow. At 2806, a subset of executable code may be activated,responsive to identification or selection of the user input. Whenexecuted, the code causes presentation of an interactive overlay windowto convey interactive summary characteristics for a column of data. At2808, an interactive overlay window may be configured to includeaggregated data attributes (e.g., an aggregation or collection ofsummary characteristics) for a column of data associated with, forexample, an atomized dataset.

FIG. 29 is a diagram depicting an example in which a subset of data maybe analyzed to determine a graphical representation of the datadistribution, according to some examples. Diagram 2900 depicts a userinterface 2902 coupled communicatively to logic embodied in one or moreportions of a collaborative dataset consolidation system 2910, which isshown to include a data derivation calculator 2965 and an inferenceengine 2908, a user interface element generator 2980 and a programmaticinterface 2990. According to some examples, elements depicted in diagram2900 of FIG. 29 may include structures and/or functions assimilarly-named or similarly-numbered elements depicted in otherdrawings.

The logic is configured to acquire data representing annotations 2920for subsets of data (e.g., column headings), datatypes 2922, a number ofempty cells 2924, a number of distinct data values 2926, and any numberof other dataset attribute values. According to various embodiments, thelogic may be configured to determine (e.g., automatically) a suitablegraphical format with which to present summary view data in graphicalrepresentations 2928. In some examples, the graphical format may beselected as a function of a shape of the data and, for example, shapeattributes. Further, the graphical format may be selected as a functionof one or more inferred dataset attributes (e.g., annotation “Place” inannotations column 2920 may be derive or inferred from other data).Therefore, logic may be configured to present insight data in a tabularformat having a graphical representation of the data distribution beingoptimized based on inferred the dataset attributes and shape attributes.

For graphical representations 2928, the logic may be configured todetermine time-based bar graph 2972 based on, for example, a datatype(e.g., “date”), distinct values 2926 (e.g., “12” distinct valuesassociated with time-related data, such as, per month), an annotation2920, as well as shape attributes. Shape attributes includecharacteristics that describe the amount, quality, and spread of adistribution due to various data values. Examples of shape attributesinclude symmetry, a number of peaks (e.g., unimodal or bell-shaped,bimodal, etc.), a degree of uniformity (e.g., measure of equal spreadingof data), a degree of skewness (e.g., skewed left, skewed right, etc.),etc. Shape attributes may also refer to one or more summarycharacteristics, such as standard deviation, skewness, kurtosis, etc.The logic may be configured to determine categorical description 2974based on datatype and a number of categories, as well as other datasetattributes. For example, a “most common” category may be associated witha greatest number of occurrences, whereby “suicide” has a greatestnumber of occurrences, followed by “homicide,” which is the next commoncategory. Further, logic may be configured to present two (2) values(and percentages thereof) as a graphical representation 2976 for Booleandatatypes, and based on other dataset attributes. Graphicalrepresentation 2978 may be presented as a histogram based on, forexample, a number of occurrences, a datatype, a spread of data (e.g.,standard deviation and ranges of values), and other data attributes.User interface 2902 may also include user inputs 2971 and 2973 to causeany of graphical representations 2928 to be accepted (as presented), orto be recalculated for presentation into a different graphicalrepresentation (e.g., a bar chart may be presented as a pie chart,etc.).

FIGS. 30A to 30F are diagrams depicting examples of interactive overlaywindows, according to some examples. Interactive overlay windows 3000and 3015 of FIGS. 30A and 30B, respectively, depict summarycharacteristics (e.g., values of dataset attributes) and graphicalrepresentations for subsets of data. For example, interactive overlaywindow 3000 includes an annotative description 3003 for a subset of data(e.g., column 3, denoted “col 03”), a datatype 3004, and graphicalrepresentations 3006 (e.g., as text) of the different categories andnumber of occurrences. Additionally, a horizontal bar chart 3008 mayalso be generated as part of graphical representations 3000. In someexamples, logic may determine a distinct number of categories (e.g.,four) and may generate graphical representations 3006 based on athreshold number of categories 3001 (e.g., less than five categorytypes). In particular, with less than five categories, the format ofgraphical representations 3006 may be implemented.

By contrast, interactive overlay window 3015 includes an annotativedescription 3021 for a subset of data (e.g., column 7, denoted “col07”), a datatype 3019, and graphical representation 3020 (e.g., as text)of the different categories and graphical representation 3022 for morethe “five categories.” In some examples, logic may determine a distinctnumber of categories (e.g., 36) and may generate graphicalrepresentation 3020, as a textual description of top two categories, and3022, as a histogram, based on a threshold number 3018 of categories(e.g., greater than five category types).

In FIG. 30C, interactive overlay window 3000 includes an annotativedescription (“Police”) 3033 for a subset of data (e.g., column 4,denoted “col 04”), a datatype (“Boolean”) 3034, and graphicalrepresentations 3036 (e.g., as text of one of two states orcharacteristics, such as “true” or “false”) of the two differentcategories and percentages of occurrences. Additionally, a horizontalbar chart 3038 may also be generated as part of graphical representation3036. In some examples, graphical representations 3036 for Booleandatatypes may include a third category, such as “null” to provideinformation on, for example, one occurrence of defective or absent data.

FIG. 30D depicts an example of an interactive overlay window 3045including an annotative description (“Month”) 3048 for a subset of data(e.g., column 2, denoted “col 02”), a datatype (“date”) 3049, and agraphical representation 3051 as a bar chart depicting occurrences permonth.

FIG. 30E depicts an example of an interactive overlay window 3060including an annotative description (“Location”) 3063 for a subset ofdata (e.g., column 10, denoted “col 10”), a datatype (“zip code”) 3062,and a graphical representation 3066. In this example, graphicalrepresentation 3066 may include a map of, for example, a geographiclocation or region spanning multiple zip codes, whereby the map mayinclude graphical representations 3068 of occurrences (e.g., as afunction of pixel color values, or varied shades of “gray” usinggreyscale values) at certain locations (e.g., within a zip code). Thus,“heat maps” 3068 may be implemented to present variable densities ofoccurrences of “suicides” and “homicides” relative to a location (e.g.,a zip code). Other graphical representations including maps are alsowithin the scope of the present disclosure.

FIG. 30F depicts an example of an interactive overlay window 3075including an annotative description (“Place”) 3077 for a subset of data(e.g., column 8, denoted “col 08”), a datatype (“string”) 3077, anddescriptive text implemented as a graphical representation 3081.

FIG. 31 is a diagram depicting a flow diagram to form variousinteractive overlay windows, according to some embodiments. At 3102,data to form a first input via a user interface may be received as afirst user interface element. Activation of the user interface element,such as a user input, may be configured to ingest as set of data toinitiate creation of an atomized dataset. A programmatic interface maybe activated at 3104 to facilitate creation of a dataset, which mayinclude the derivation of a dataset attribute that may be used indataset creation. At 3106, a request to generate the dataset having afirst format may be transmitted to a server computing systemimplementing a collaborative dataset consolidation system. The computingsystem may operate to interpret a subset of data (e.g., a column) of theset of data (e.g., a number of columns) against one or more dataclassifications at an inference engine to derive an inferred datasetattribute for the subset of data.

In some examples, interpreting a subset of data may include identifyinga data type for an inferred dataset attribute, determining shapeattributes to form the a graphical representation of a distribution, andselecting a first graphical format based on, for example, a datatype. Anexample of the first graphical format may be a histogram. Interpretingthe subset of data, at least in some cases, may include identifying adata type for an inferred dataset attribute as a numeric data type,forming a histogram user interface element as a distribution (e.g., agraphical representation) for presentation as summary view (e.g., withinan interactive overlay window) in a first graphical format based on thenumeric data type. Further, interpreting the subset of data may includecausing presentation of a histogram user interface element in a userinterface.

Interpreting the subset of data may include identifying a data type (forthe inferred dataset attribute) as a categorical datatype, according tosome examples. The categorical datatype may be associated with a valuefor data points (e.g., a value representing a number of categories)within a threshold amount (e.g., a threshold number of categories) underwhich a graphical representation (e.g., histogram) may be implemented.In some examples, interpreting the subset of data may also includeforming one or more textual user interface elements as a distributionfor presentation in a summary view in a text-based descriptive formatbased on the categorical datatype and number of categories. Therefore,textual user interface elements may be presented in the user interface,whereby the textual user interface elements may specify one or morecategories having greatest values.

By contrast, identifying the datatype as a categorical datatype mayfurther include determining the value of the data points exceeds athreshold amount (e.g., more than 5 distinct categories may trigger achange in format of a graphical representation). That is, when a numberof distinct values for the data points exceed a threshold amount,another graphical representation may be implemented. Further, ahistogram user interface element may be formed as a distribution forpresentation as a summary view based on the categorical data type. Thehistogram user interface element may be presented in the user interface,whereby the histogram user interface element specifies a distribution ofa number of categories.

At 3108, data representing a distribution in a first graphical formatmay be received for a subset of data. The first graphical format mayconvey visually a shape of the data. In some cases, a second input maybe implemented to recalculate a second graphical format with which topresent the shape of the data for the distribution. Subsequent toactivation, the distribution may be presented using the second graphicalformat. At 3110, data representing a distribution in a summary view maybe presented at a user interface for the subset of data. Thedistribution may be a summary representation that represents a shape ofvalues for one or more dataset attributes associated with an inferreddataset attribute.

FIG. 32 is a diagram depicting an example of a dataset access interface,according to some examples. Diagram 3200 depicts a user interface 3202coupled communicatively to logic embodied in one or more portions of acollaborative dataset consolidation system 3210, which is shown toinclude a query engine 3230 and an operations transformation engine3240, a user interface element generator 3280 and a programmaticinterface 3290. According to some examples, elements depicted in diagram3200 of FIG. 32 may include structures and/or functions assimilarly-named or similarly-numbered elements depicted in otherdrawings.

In this example, user interface 3202 may be implemented as acomputerized tool that may be configured to access or otherwise query acollaborative database through a data entry interface 3204. According tosome examples, data entry interface 124 may be configured to acceptcommands (e.g., queries) in high-level language (e.g., high-levelprogramming languages, including object-oriented languages), such as inPython™ and structured query language (“SQL”), as well as dwSQL (i.e.,as dialect of SQL developed by data.world™), among others. Further,commands in a high-level language may be converted into a graph-levelaccess or query language, such as SPARQL, other RDF query languages, orthe like. Thus, a query may be initiated via data entry interface 3204to query data associated with an atomized dataset, including linkedcollaborative atomized datasets. In some examples, data entry interface3204 may be configured to accept programming languages for facilitatingother data operations, such as statistical and data analysis. Examplesof programming languages to perform statistical and data analysisinclude “R,” among others.

Further to diagram 3200, operations transformation engine 3240 mayinclude a “SQL-to-SPARQL” processing engine 3242, which may beconfigured to transform SQL-based commands into SPARQL or other RDFquery languages. Operations transformation engine 3240 may include an“R-to-RDF” processing engine 3244, which may be configured to transformstatistics-based commands (e.g., R) into RDF or other graph-levellanguages. Further, operations transformation engine 3240 may include a“Python-to-RDF” processing engine 3246, which may be configured totransform Python-based commands into RDF or RDF query languages. Notethat other transformations between high-level programming languages andgraph-level languages are possible.

In view of the foregoing, a user need not rely on graph-level languages,such as SPARQL, but may implement high-level programming languages thatmay be more familiar with users and data practitioners. In someexamples, an overlay window 3220 (or any other user interface element)may be presented concurrently (or substantially concurrently) with thepresentation of data entry interface 3204. Thus, as a query 3272 may beentered in a high-level programming language, a low-level version (e.g.,in SPARQL) may be presented in interface 3274 in a mirrored fashion.This is, interface 3274 may be referred to as a “mirrored data entryinterface” in user interface 3302. Mirrored data entry interface 3274may be configured to detect entry of an operation instruction in dataentry interface 3272 and replicate a transformed operation instructionin mirrored data entry 3274 as a graph-related data instruction (e.g.,in real-time). Thus, users that are less familiar with low-levellanguages may begin to learn or adapt queries from entry and thehigh-level programming language to, for example, SPARQL or the like. Bycontrast, users that may be familiar with both high- and low-levelprograms may wish to validate an appropriately crafted query byreviewing interface 3274 while the query is entered via data entryinterface 3204.

FIG. 33 is a diagram depicting a flow diagram to implement a datasetaccess interface, according to some embodiments. At 3302, data may bereceived to form a first input via a user interface as a first userinterface element. The first user interface element may be configured toinitiate a data operation on an atomized dataset based on a set of data.Examples of a data operation include dataset accesses, dataset queries,statistical operations on a dataset, etc. At 3304, a data signalindicating selection of the data operation may be received (e.g.,initiate “run query”). A programmatic interface may be activated at 3306to perform a selected data operation responsive to the data signal. At3308, a data entry interface may be presented in the user interface toreceive operation data instructions. The operation data instructions maytransform the data instructions into graph-related data instructions, at3310, to access data associated with an atomized dataset stored in, forexample, a triplestore repository. At 3312, graph-related datainstructions may be implemented to perform the operation (e.g., at alow-level). At 3314, data representing results (e.g., query results) maybe received responsive to executing the graph-related data instructions.

FIG. 34 illustrates examples of various computing platforms configuredto provide various functionalities to components of a collaborativedataset consolidation system, according to various embodiments. In someexamples, computing platform 3400 may be used to implement computerprograms, applications, methods, processes, algorithms, or othersoftware, as well as any hardware implementation thereof, to perform theabove-described techniques.

In some cases, computing platform 3400 or any portion (e.g., anystructural or functional portion) can be disposed in any device, such asa computing device 3490 a, mobile computing device 3490 b, and/or aprocessing circuit in association with initiating the formation ofcollaborative datasets, as well as analyzing and presenting summarycharacteristics for the datasets, via user interfaces and user interfaceelements, according to various examples described herein.

Computing platform 3400 includes a bus 3402 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 3404, system memory 3406 (e.g., RAM,etc.), storage device 3408 (e.g., ROM, etc.), an in-memory cache (whichmay be implemented in RAM 3406 or other portions of computing platform3400), a communication interface 3413 (e.g., an Ethernet or wirelesscontroller, a Bluetooth controller, NFC logic, etc.) to facilitatecommunications via a port on communication link 3421 to communicate, forexample, with a computing device, including mobile computing and/orcommunication devices with processors, including database devices (e.g.,storage devices configured to store atomized datasets, including, butnot limited to triplestores, etc.). Processor 3404 can be implemented asone or more graphics processing units (“GPUs”), as one or more centralprocessing units (“CPUs”), such as those manufactured by Intel®Corporation, or as one or more virtual processors, as well as anycombination of CPUs and virtual processors. Computing platform 3400exchanges data representing inputs and outputs via input-and-outputdevices 3401, including, but not limited to, keyboards, mice, audioinputs (e.g., speech-to-text driven devices), user interfaces, displays,monitors, cursors, touch-sensitive displays, LCD or LED displays, andother I/O-related devices.

Note that in some examples, input-and-output devices 3401 may beimplemented as, or otherwise substituted with, a user interface in acomputing device associated with a user account identifier in accordancewith the various examples described herein.

According to some examples, computing platform 3400 performs specificoperations by processor 3404 executing one or more sequences of one ormore instructions stored in system memory 3406, and computing platform3400 can be implemented in a client-server arrangement, peer-to-peerarrangement, or as any mobile computing device, including smart phonesand the like. Such instructions or data may be read into system memory3406 from another computer readable medium, such as storage device 3408.In some examples, hard-wired circuitry may be used in place of or incombination with software instructions for implementation. Instructionsmay be embedded in software or firmware. The term “computer readablemedium” refers to any tangible medium that participates in providinginstructions to processor 3404 for execution. Such a medium may takemany forms, including but not limited to, non-volatile media andvolatile media. Non-volatile media includes, for example, optical ormagnetic disks and the like. Volatile media includes dynamic memory,such as system memory 3406.

Known forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can access data. Instructions may further betransmitted or received using a transmission medium. The term“transmission medium” may include any tangible or intangible medium thatis capable of storing, encoding or carrying instructions for executionby the machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Transmission media includes coaxial cables, copper wire,and fiber optics, including wires that comprise bus 3402 fortransmitting a computer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 3400. According to some examples,computing platform 3400 can be coupled by communication link 3421 (e.g.,a wired network, such as LAN, PSTN, or any wireless network, includingWiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.)to any other processor to perform the sequence of instructions incoordination with (or asynchronous to) one another. Computing platform3400 may transmit and receive messages, data, and instructions,including program code (e.g., application code) through communicationlink 3421 and communication interface 3413. Received program code may beexecuted by processor 3404 as it is received, and/or stored in memory3406 or other non-volatile storage for later execution.

In the example shown, system memory 3406 can include various modulesthat include executable instructions to implement functionalitiesdescribed herein. System memory 3406 may include an operating system(“O/S”) 3432, as well as an application 3436 and/or logic module(s)3459. In the example shown in FIG. 34, system memory 3406 may includeany number of modules 3459, any of which, or one or more portions ofwhich, can be configured to facilitate any one or more components of acomputing system (e.g., a client computing system, a server computingsystem, etc.) by implementing one or more functions described herein.

The structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or acombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated with one ormore other structures or elements. Alternatively, the elements and theirfunctionality may be subdivided into constituent sub-elements, if any.As software, the above-described techniques may be implemented usingvarious types of programming or formatting languages, frameworks,syntax, applications, protocols, objects, or techniques. As hardwareand/or firmware, the above-described techniques may be implemented usingvarious types of programming or integrated circuit design languages,including hardware description languages, such as any register transferlanguage (“RTL”) configured to design field-programmable gate arrays(“FPGAs”), application-specific integrated circuits (“ASICs”), or anyother type of integrated circuit. According to some embodiments, theterm “module” can refer, for example, to an algorithm or a portionthereof, and/or logic implemented in either hardware circuitry orsoftware, or a combination thereof. These can be varied and are notlimited to the examples or descriptions provided.

In some embodiments, modules 3459 of FIG. 34, or one or more of theircomponents, or any process or device described herein, can be incommunication (e.g., wired or wirelessly) with a mobile device, such asa mobile phone or computing device, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with one or more modules 3459 or one or more ofits/their components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figures can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, modules 3459 or one or more of its/their components, or anyprocess or device described herein, can be implemented in one or morecomputing devices (i.e., any mobile computing device, such as a wearabledevice, such as a hat or headband, or mobile phone, whether worn orcarried) that include one or more processors configured to execute oneor more algorithms in memory. Thus, at least some of the elements in theabove-described figures can represent one or more algorithms. Or, atleast one of the elements can represent a portion of logic including aportion of hardware configured to provide constituent structures and/orfunctionalities. These can be varied and are not limited to the examplesor descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit.

For example, modules 3459 or one or more of its/their components, or anyprocess or device described herein, can be implemented in one or morecomputing devices that include one or more circuits. Thus, at least oneof the elements in the above-described figures can represent one or morecomponents of hardware. Or, at least one of the elements can represent aportion of logic including a portion of a circuit configured to provideconstituent structures and/or functionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (i.e., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described inventivetechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described inventiontechniques. The disclosed examples are illustrative and not restrictive.

1. (canceled)
 2. A method, comprising: identifying via a network acomputing device of any number of computing devices distributed amongthe network, at least a subset of the computing devices associated withone or more data repositories configured to access portions of datasetsconverted into one or more atomized datasets; and causing access via oneor more programmatic interfaces including executable code configured tointerface the subset of the computing devices to implement one or moreportions of an application disposed in the subset of the computingdevices to implement a computerized tool to perform one or more dataoperations including: presenting a first interactive user interfaceelement configured to receive data representing one or more commands toaccess at least one of the one or more atomized datasets; activating aprogrammatic interface to modify an atomized dataset; modifying theatomized dataset based on data in a data file; calculating automaticallya subset of dataset attributes to form the atomized dataset as a linkeddataset to form the subset of the dataset attributes; transformingingested data in a first data format into a second data formatcompatible with the one or more atomized datasets to form ingested dataas the data file; and presenting a data arrangement overview interfacesummarizing the subset of the data attributes as an aggregation of dataattributes in a portion of a user interface, at least one data attributeassociated with the ingested data.
 3. The method of claim 2, wherein theone or more programmatic interfaces comprise one or more applicationprogramming interfaces (“APIs”).
 4. The method of claim 2, wherein atleast one atomized dataset comprises: a graph.
 5. The method of claim 2,wherein at least one atomized dataset comprises: triple data.
 6. Themethod of claim 2, wherein causing presentation of the first interactiveuser interface element further comprises: receiving commands configuredto implement SPARQL.
 7. The method of claim 2, wherein creating theatomized dataset based on the data in the data file comprises: creatingthe atomized dataset based on raw data in the data file.
 8. The methodof claim 2, wherein calculating automatically the dataset attributes toform the atomized dataset as the linked dataset comprises: calculatinginsight data.
 9. The method of claim 2, wherein calculatingautomatically the dataset attributes to form the atomized dataset as thelinked dataset comprises: deriving at least an inferred datasetattribute.
 10. The method of claim 2, further comprising: presenting asecond interactive user interface element configured to implement datarepresenting the data arrangement overview as an overlay interface. 11.The method of claim 2, further comprising: providing access to the firstuser interface to metadata describing one or more dataset attributesincluding one or more ranked dataset attribute values of a subset ofranked dataset attribute values.
 12. The method of claim 2, furthercomprising: transmitting authorization data to authorize access to theatomized dataset including raw data values, the atomized dataset being arestricted atomized dataset associated with one or more other usercomputing devices of a community of networked user computing devices.13. The method of claim 12, further comprising: linking the restrictedatomized dataset to the one or more atomized datasets to form acollaborative atomized dataset.
 14. The method of claim 1, whereinmodifying the atomized dataset comprises: a database configured to storedata as triples.
 15. The method of claim 14, wherein the databasecomprises one or more triplestores.
 16. A system comprising: a memoryincluding executable instructions; and a processor configured to executethe instructions to: identify via a network a computing device of anynumber of computing devices distributed among the network, at least asubset of the computing devices associated with one or more datarepositories configured to access portions of datasets converted intoone or more atomized datasets; and cause access via one or moreprogrammatic interfaces including executable code configured tointerface the subset of the computing devices to implement one or moreportions of an application disposed in the subset of the computingdevices to implement a computerized tool to perform one or more dataoperations including: presenting a first interactive user interfaceelement configured to receive data representing one or more commands toaccess at least one of the one or more atomized datasets; activating aprogrammatic interface to modify an atomized dataset; modifying theatomized dataset based on data in a data file; calculating automaticallya subset of dataset attributes to form the atomized dataset as a linkeddataset to form the subset of the dataset attributes; transformingingested data in a first data format into a second data formatcompatible with the one or more atomized datasets to form ingested dataas the data file; and presenting a data arrangement overview interfacesummarizing the subset of the data attributes as an aggregation of dataattributes in a portion of a user interface, at least one data attributeassociated with the ingested data.
 17. The system of claim 16, whereinthe one or more programmatic interfaces comprise one or more applicationprogramming interfaces (“APIs”).
 18. The system of claim 16, wherein atleast one atomized dataset comprises a graph.
 19. The system of claim16, wherein the processor configured to cause presentation of the firstinteractive user interface element is further configured to: receivecommands configured to implement SPARQL.
 20. The system of claim 16,wherein the processor is configured to: present a second interactiveuser interface element configured to implement data representing thedata arrangement overview as an overlay interface.
 21. The system ofclaim 16, wherein the processor is configured to: access the at leastone of the one or more atomized datasets in accordance with a resourcedescription framework (“RDF”) data model.